{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "from keras import layers\n",
    "import numpy as np\n",
    "import os\n",
    "import shutil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_dir = './dataset/cat_dog'\n",
    "train_dir = os.path.join(base_dir , 'train')\n",
    "train_dir_dog = os.path.join(train_dir , 'dog')\n",
    "train_dir_cat = os.path.join(train_dir , 'cat')\n",
    "\n",
    "test_dir = os.path.join(base_dir , 'test')\n",
    "test_dir_dog = os.path.join(test_dir , 'dog')\n",
    "test_dir_cat = os.path.join(test_dir , 'cat')\n",
    "dc_dir = './dataset/dc/train'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists(base_dir):\n",
    "    os.mkdir(base_dir)\n",
    "    os.mkdir(train_dir)\n",
    "    os.mkdir(train_dir_dog)\n",
    "    os.mkdir(train_dir_cat)\n",
    "    os.mkdir(test_dir)\n",
    "    os.mkdir(test_dir_dog)\n",
    "    os.mkdir(test_dir_cat)\n",
    "\n",
    "    fnames = ['cat.{}.jpg'.format(i) for i in range(1000)]\n",
    "    for fname in fnames:\n",
    "        s = os.path.join(dc_dir, fname)\n",
    "        d = os.path.join(train_dir_cat, fname)\n",
    "        shutil.copyfile(s, d)\n",
    "\n",
    "    fnames = ['cat.{}.jpg'.format(i) for i in range(1000, 1500)]\n",
    "    for fname in fnames:\n",
    "        s = os.path.join(dc_dir, fname)\n",
    "        d = os.path.join(test_dir_cat, fname)\n",
    "        shutil.copyfile(s, d)\n",
    "\n",
    "    fnames = ['dog.{}.jpg'.format(i) for i in range(1000)]\n",
    "    for fname in fnames:\n",
    "        s = os.path.join(dc_dir, fname)\n",
    "        d = os.path.join(train_dir_dog, fname)\n",
    "        shutil.copyfile(s, d)\n",
    "\n",
    "    fnames = ['dog.{}.jpg'.format(i) for i in range(1000, 1500)]\n",
    "    for fname in fnames:\n",
    "        s = os.path.join(dc_dir, fname)\n",
    "        d = os.path.join(test_dir_dog, fname)\n",
    "        shutil.copyfile(s, d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(1) 读取图片\n",
    "\n",
    "（2）将图片解码\n",
    "\n",
    "（3）预处理图片，大小\n",
    "\n",
    "（4）图片归一化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_datagen = ImageDataGenerator(rescale=1/255,\n",
    "                                  rotation_range=40,\n",
    "                                  width_shift_range=0.2,      \n",
    "                                  height_shift_range=0.2,\n",
    "                                  brightness_range=(0.6, 1),\n",
    "    shear_range=0.2,\n",
    "    zoom_range=0.2,\n",
    "    horizontal_flip=True,\n",
    "    vertical_flip=True)\n",
    "test_datagen = ImageDataGenerator(rescale=1/255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2000 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "train_generator = train_datagen.flow_from_directory(train_dir,\n",
    "                                                    target_size=(200, 200),\n",
    "                                                    batch_size=20,\n",
    "                                                    class_mode='binary'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YGcahmILqBzpngRtWhYP3KWds2vuTE/AEZWVLE424rPwq4nvA6+3zI2XAmSeYiEGswzpL07bUC/Xh63aBLVYDxmE9SIKUt4h1pJRYniy5/3AJgPdVoTVnqroijAM5R16+fs0Yk96ZUfYe6GRT01Tp3M45JOuKFVDQ0nrHOKhSSGQYOqXmooxCMYIsKtpFTW0M26srXj97ruePCW8McRhp2oa466gtECPGWBWqkk8BsBki1DXVYkHdNNjVGbQ11xaiBF69fkO0AgUEVFJPYQyWcxgUQI0xFsWfiOwBOWOmlb9YCaI/kigzZ8EI8z3p/NGVPzOBo0UtWIt1npAVU9HRBzEWYx1VVVFVFe1ySd3UhBjYbLa8fPGCbqPgrZE93jMxFclCJM0YlzABopDNHo/hPfjY+9pHrxRijLNwj2EkhlCUwM0CD8dCPwv8pLdzPuJxHLIYU04Mw4A1Fmd1Mjp1do+On/Mh0K/iQX7EMY318Kbejlho3oQtQFCK6Qj0m2i+KSacV8qsdZ7lyRnL5Yohxfmc22GkWQpJhKqusblSLoLoatNUFc5aFqsTfYYEyWaqytE2NV23w5C5vF4TwkBdeVzOM5suzPe1Z+OlGRhNWGc1f6DT+1Eh1s7JIRJEoyVrE6mNw7Y1zeKCpdczjNuONAa2l5dsup6qthjX4KwliWXIGeM89XIFwKOzM2xTMebMLo70JjPkxJAjY4okb0gGCiOKTCaSClnKFnM9Y0gF0jFFoParezwYz+Oksj1ol4pimGZdFlEacnEVJpo2SefMdtPRNEqoahYt7WLByckpp+dnGgER0eQyIk17grUVL5890zFer9W6MQZj3Z58Nkcg4juCrxaNRo5+EQ/io1AKKSZev359ZN6H8CECP1nuR1I7C/2hwM8yWczA+XwHRJ0cSkjpANQJIR4df5RRWPxqYEb2y1E6UAdIRAjj7AZM2W/DONLUNd57Rg6eN0VdRcRDSlhbkUVolytOz89ZnpzqSlNCjHWzYLFc0DQLqqpiTIEQd4QY2W63XNy/wBk3T8iUDWEcqSqPcQ7nA9vtmm3XYwzYlLBqHJdnTqSkAuysJQwjpEQ2gmTBi8GKoV2oJdKPA9bZWaklq6DZCMQcGfodlXX4O6d6zrNTnBi+WP0WdVXhrGPz5hpJ0PUDu92Oqm6oWrWMpPJ0KbIdBrYpE1F3YkyJEAPZFKCuYBzWGeraU2VwRZemXJRwylir9xpjmIFDTcjS/IgJx1E3T2ZhzzmU5LL9XLDWYkrSXCzYk4gUl85QVToG9+59wmKxYLFcqrUWJ9PfkjPUteXhg0+onOI5z588pt/tIGescYC6xCrwhXL9NmtRiksq8IugCh+FUhjDyOtXr98ReDgU+gn9nT6/QeDLD+ekkAOBn37PQVp0+VSPpQA7RkG2ALRWM9HSO+FHvbHD9FRrjCLTZbJNVgeAs04njZWCEQi+algsl0ppzUlR5vJM3W5HzglrLb6qEWMIKdG0C8RYIrBYLlksdeVv2gXGGmpf4b0n9cKYNnOqMGJpFgvqtqSnJ0usItYaxjiQBDa7HdZZSJE4jAwI2U7hyYRxFsEQ41hyFUAKvjCOisGkKW/AWMYSTVEehJBE6cTZWBDDmCJNwSyct4SYMDnSx4HKCENd4cUSy7MnZ9kWHGgce4YcycYQjRCNMPQD3TioUnMGK0phBnCugL5JAeFUAMRp+NTiVNDOzIxGdfAEo4ot6nikHEmxhB8lE6bFIO4XD1MUhTmIRDRNy3J1zvm5siwvLu5hrQVjSEn5JyknkKTuJkJdVZzfuVvGDF4+f8Zusy7hSSEzYI074F28FSrPprg/5i+fpQBlNXrLPIP3r/I3CjwcCf2hwB+67Kb4WXrd/I7imejBISrByByEz2YXYf69amiNLhWTzgiVc3hvy/WMUlCNwVqHsZ52uaRtW+WvJ51wYwECQ4isN2u8tXjrFVlGsNaz6wawntXp2Szkpqx0IWas08hHiplmseD0/BznHMY56olHEDKM6keHNPLy6g3PX78AgXHoQdSHD1NuNuByxhnNyJNCARZUoannkzFmv2raDMM4FAA2YSQRQiCJoa5qxAh9geKTNVhvebm5YugHFoslBo/FEmNkQOstTMlCWTIDSnoaxoGUIinHmQtgnSrePe8DxjhgxTCGWOaN+v7W2kIFNgzjOPvwVaUp584VC0I9eYw1eOfIMZMlI3P0SOfbWEDqmRAlQlXVLJZnLJdnLIsib5uWPM86IeeREDMhjtjKaaRnGGdhXq1OGIee3XarqeRWXRVjjZKfrJldFu1UyES1KrIc+jnf2T4apXCTwANHQp9vEvgbntUUgOhQ4HMBAY8AIPYWx3ScHGDe+Ybj9rcrM7YwfX84QUQEX6IDRsxsRlZ1hRhPFjR3PyXapmWxXBALUGedYwgjzhqaxYKYEs57qqbl1ZtLTs7v0LaL2R0IKbJdb3E2Fg69Zt61ywWr1QnGGpq6mXtzDD2Umguv3rzm1euXvHr9qoRkA5KmKMleKYQQwWZ1K4q0pVIHwBaXaOq3VBiSxhhl2aVUBMqCWMYQMdbM3I4h6POFGBliJA8DQiTFrAJmHYa95SXWECUzRFUKTgyVrxADQsYBJu3rWeSQcAVUTFkwziBiiCkQU2IMESEqjlBwlBiT4j1J1YERU6I3mdGOGiEx4As3YxLOvutIOXN2dqaYVNOwXKw4PTtnsTjFF/cBkRISNsSUsTGSEbWKInhnGPqeOA7TDGSxOuHLHy54c/mGq6tLTJUKa1FzJ3LBxWAqhVEiEDnjffWuoLyn3eY+3LbbdtuO2sdjKdxgBcCxJTAxxo6sgFxAvSm8zGQJTDTevbUxh4oO28F7M1krMc3HTtELOXI3bgrvKP8gpKDAzwEjb1pxQwiKREtk2O0YxxERoa5qyGoiAqxOTktYVDg7OSXGjPGKC9y1jrM7d0oIstxT1GIp0qqF4JyjqmvqumK1Wqr1gNB3HQDr6zW77ZYxBK7Xr3n59BmXL19BDOSgFaVq76nrAuwZUbcCORqbXNKy1aWKGntFTdssE7dfQdhoDL5EdUJIhdlJ+T6wXC6xxrDZdoTdFjClSEkGieRIid9rlACrNQWa2uJxSE5U1mtfT3UU4jy1cN4yEuljJMYMoqFXVzmqtqFtFqxWqznSkMNU+0BDk95XdF3PdrtmvV4TQyblYbZeYlTrh5xp2wXLxRJbsCRf1RhrSfmg/wxkK3NNhEhCnMFjyEFDypKN0p1RVynnTDcMtO0C7z3XV28Y+46+78koYDoW5qtiXDrv33bJv6t9HEoh3yzwMAFVB37/DQJ/VPri8OmP/i7XKG/3dQQUH9i/L3hkmbE3FT458G6K0pry4G3hsOtdTSCUL/5lCIFdt8PYCqxFvFMTtYCSE9J87/49MMJuu6PrBharBVXT4L3n7G5Nu1ziXTVHPGKM+MrTNg0Z5Vo0TcNisaBt2xkFnxKunj19wjgM+Mrz+GffsL6+xhvLyEGJuFKaDhQjGIdh7ncNjZV+K2M0hUAB6rolsS86EksIM6cIIWFyJk/AGirk3XanYz8Jfh4RW/omUu7pwLAVwYhVLkSKWDFK7MpR3RcjGroDnFeizzD2RDRNfLU6oa1bzs7PadqaRbuk8jWxFHHzxhaWawLR8KyIZQwDV1dXdN3AZnPF9eYSgPX1FYvFkrOzc05PNMQ4hWWtq7CuhtoymmmxEAhhLoqitHVRgpbowmOtQSqdEykZMJmnT5/Qti2rkxWQGDqPMZau6xjDSN93ZQxqpdBDKeTyj7nIyi/etMbcHArUj5heZjjxOwQejoV+5pYfgoT6x/zTt9/HGI94CDeBFtNkn4RkqrE35d7r94YpEBRCKXgRMzkLzlissfic8cZiMvS7HSnqM11cXHD/3gO++fljsmi9RO8sJ6en1HWjhB1Xzf3ifcR5R1VXxELXnvrUOodJea7dCDCGwPNnz+mHjm67JcU4A4ikRCq/HQZlHFrnZsvHMsXnC/hqjNZ2SPsqRADOemIRZPGuxNPVdwZDzgFXZNx5jwcSidB36m9b0QSmrIr7sH6BiIYc9TkjOWo8fgyFsem9ZhvGPV4E0CxWVG1DVVWc3z1n0Sw5PTtVbCdPo1UwAl9hbaYqhKYYCx+jqTG+YrvrqNuWiwef6BiPkarS/rdWmYqVmEJ8Qynfsq+MNLNYjcwWr3KfJkakzs1JMacESOL+gwfsdjtyyiwWK5btgpPVWclx2bJZb+f+T2koi+meY/Ih7aNQCjlzzEt4x8R/d5Wf6J+HAq+vzEe+T6jfvf5e6RzWBjys4Du9Giak3cwKgVyKbY0Rbz0w1dwrv9VYF9Z5XFVhraVyFu/17xgCm13HMLwBoOs6fFUxDoFt39E0DSenJ7Rty2KxJE7pznYKuWlYyjqnTDz0vkRkDqUZY6i8Jkmdn5/x5Jtv+Mk//Efcv3eBQRj7nhyjrsY5a7k20VXH+2pOwBpjxFvDnp+vUSPDHpRMUa2GFLUwyGTVqWWgIducE3Yyda2GDHPKOKt9Z4wrOSSCxRxxRWIuKegpEQcFP73VSAUlPdpYR19M+8VixWK55M69e5wU4LWqa33uEBXNFwXrbLEujJ1cB32J48iYArv1jl2nEQZb2JYAdeOxzmJLzHxalCY31IgWcZ0Kw+r4GELIGuUqfZ+zx6Lp8tbZGfaOcSQW0NU7T9M0Oibl+c7OzsnIzBXxlefq6pqu23G1uZ7Dsx/SPgqlQM7IUWfuH+CdUOD0uX74Aad+28q4wb/KN+MNqVguYu0+Bm10wDKafJJLVGSa6AIYScrGm5WTosy+qrBGQ2CVU1cj9D19P3C9Wc+5CdY5Tk/P2Gw3vL685Pz8nLquZ6KTFJKLsZPPnnCuYBcWUgpkXCHlJFJSKnXTqlJompqmaWiqmrEfSClADBgN8ZByZAxxrtQEE8VZOQzS1FgT58QvDf/Zo7BXsaOU3pxmlhlJ1GpKOc7K36HVkkOOWG+1kK0YUlITOiatGTmRczJZCUpZMyPFCBiwBVNICba77TzO/+Rv/AZt22Cdo27aYtYrHmKdx1uLc+rKzXkEMTCOAyFoibvr6zU5Z4ZB0X7v9VpDiQ7YUqhVSrLVdJ8xaG1HYyx9tyMcuKXGGEIM7HY7BKGqKnXJjMWUondHrnGCzXpDPww45xiGkRhHqqqmrmsu7j/gzsU9QDGmx48fc3V9pbU73sP+van90kpBRL4A/lvgE9Ty+t2c838hIv8x8O8Bz8uhf6OUZvu2c83hO/hVBB7eFvqbjzmgLRxcxxijFsvkK6N565ISacqDT1ELmkyDW/4zlejOpTR6VVXEt3AIV3n1dZ3FeV+or1u2ux3r7WYO0a3Xa7788kuatsVXFScnJ5ydqZ/a7XRiOefxUu3P7exsJioOZ7RAS1GwU9nw8qBYK3T9ThOByPqvsPiSKE03jOo+mJjIKeqqLUZ5ARGwjv1axvyXUIqLWkuYWHelQzWDwRDJpbgKmDgiYrVKslXLIKZATuqC5VIgdepNMVpqzhpDZQRJiTH0pZ5FIKbM519+yWeffQbAyemSBNRtixE75wdYcXjvadqGbtcxhHEW6GHo2HU7LYcXAuOofBXnLNbo78iZuvj8+wKrzNaFES3+mourE8Ywl8mUku1isv6z1uCtw5XwJyRSDDPuApr4NBHYdtuubDGg6dL9MGCtpoaDulAXFxd47zk9OcFay+P/7X/nQ9qvYikE4D/MOf+BiJwAf1tE/pfy3X+ec/5PPvhMwpEVoIJ9A3iYD/Xm+wUejt0I2Fe5mf23w98euA3mwMzSwiO2VMY5ACY5rk2gMX2HwH7zk5znVGURjdE758AYKu9xxpR6DHHGMaZVqu96FosFDx48ZLla4UvCTIyR9WZD3/e4qp6jFb7ymvRTyEGTOzGxJSn1+iZT1zvH68s3vLm+xhg4Wy4hpHllJ5WCnwUisIWJaYtvn7PmeyARawpr72AcUo5YtLbDFGafhkMLBiWSyTOPQOK4XxMFMBDHiCsrZiwMkv3s0H6WHNltOsZhpA8jwziyXK149OhTvvzqKy7KqhkTSEqIWC1ZI1pLwVqngGAIbDabGUMBrZsxjj0ihqryOFdhnVUwORbA25g5acyEGwD6AAAgAElEQVQaU/ZZKOSxVMrVF1fH2kKKK/NIC+QI3lhqPyl3mYlyMaUScZgmmVLMl4sFdaVJUynmWWkMw0COEdcWJeVElV3fUdcV5+fn78jI+9ovrRRyzo+Bx+XvaxH5I3RnqF/iZBytBOWcN/4N7xH4A7LTlLAzRSsmgZ8jHGZPUZpM4MMoQ5ozGg3GFQrupCymkKSIgnIHCK8WP9JMuXEM8y0aY0oFJPXBc8qEpGYpoii/s5aukJeqSk3TxXJJP47zM8UYePr0CcMYePjJJ4QwkZ2snr8U1RDR1XRKfY6xsPhKNy5XK84v7rJctgpMplLiDTXFU9aQbJxCeyN4Z7WgSY5aBCSWZBxbBETyvMrGEDEuzv0xA7NFShRITnO0IiGYknk5lsI11hhyiAxR+8g6w5SupBvf6CVd5RBrcKnB7Lb8+q//mK+++prlclVyBPQX1mgfjSHQ1I368zHovhfDwGazUZ/d7UViKtY6bwaTmcOOtij5WRHGpNGOYiVkY+bKW3pOS12SpaY2l3MvgKwpSXIpxplBNK2VufSdEYuvLCaqcgoxkpMuKjFljFUtXNUVxmhBnD//6Z/z88ff8KHtLwRTEJGvgX8a+D3gd4C/LiL/DvB/oNbE62/7fSbPqOzbAg97oZ/KSr1P4KH4l4Vd97bA33zxd/GEQ/xCCmYQ8kHQOyk0XNQIivSn+R6nMOMMeBrlz4cQiiAMGLREeEq6k49zju7qaj5+qupU1/XMlbi6uub6+rpMVnOUADNV5AFKAg9lku3Dq1Or25Yf//g3efnsGS+fP2cYe2rvcIW+bURpx1Pl4dAn9ccrX86l6D9ZA11SQpizgISICWPBYsxsjYEWCjFktHJxGV9b4Wx91GfGGMSVWhII4gxuEpDyaq0FgX4YQRz3Hzzkhz/8IYvFCWOMpLAvmEIJ92k9BcVIttutrrDFQnTOzYpUoyUaMpyiD6Crfd1oBIOsSW2gpdtjKX+XpwzJMrfSZDWKHIzRRBeXWaea0ofxIPI1TctpCiu2YxAM1mq1JsRgrCekYQZXM2rhtYuWru/5yT/6yfsk4J32KysFEVkB/wPwH+Scr0TkvwT+Zrmvvwn8p8C/e8Pv5m3jvLMzMKUL6jSJ9hEBKeDT5O9/iMB/m3sx3wdvuy7z/QGUCkv7UmlkJdFMrgVT3kEY5kKdYtVvnWLDxqp/vV+JM6mkgMeotSCurq9mq+NHP/oR5+fnKtyi2MB6s+HlixdK+22d1hkopKh9PoYpK/M+q7HI+ew2gQrT2d1zvvj6azKZV48fz+GdCTSsrJ9BLqkty8WKtq0Zx0Dfd/RDp9KZD+sLlGsaAwXcrG3FEAfNDzFTqfO8vyl0UQzjSFVXNHWNZC1Hl1MpFEupx1CGKWTd98BaSz9qFuWjT7/kt37rr3B6cq4gaalVAJS9L8CIQ2ym6zombshkFXrvj6wEgO225/LqitVyqXtmOocvSWfkPIOQ0zyagOk0z1k5UhBGZAaHVRMwRzv2U1Dp5Vm55nP1q6mlGJUwVmpxTPRlYyzj6Oew7BRWd87y6NEnXF6+5tmLy/fKwWH7lZSCiHhUIfx3Oef/UZ81Pz34/r8C/qebfpsPto1b1FWOsybdC3w5x43X/lCB/xDAcr7GQShyatPKOGmFGfgxRY3lTBxHDbEZrY4UU4lE2KlIpykAoBJ2QojkGInjQIix1HwMUITqs08/5e6du7y+vFTT33hicRXaxYLFoi0+8bFSMNYSk8y7Gel3Bihl5so5dEJHLu5fsN1uePrzbzAhYHyloVNjNDxXzG9fL2nbBd3Qs4uBdR/IpTJRDBnvDY58FAuf60sc5E8Y0L5hwk/SfD9kVahVVWHQFPZpz05KvYMpupGGhKkMIhYJwmK55NNPP+Ps/A5DCPsxm1dXGGIglPyXcexJMc5WWAhhHvPJqspoBa6+67DGsFpZmropwKJhLGHE2To6qMUwjck8lwTEmjn1et8/+jqlbs8Yi5mqZ++Pn/vQWnRvF41+iZSEKGNKGnfhQZSFaxwC5yfn/PjXf5NnL36PD2m/dO6DaC/+18Af5Zz/s4PPHx0c9m8Af++XvcZtu2237f/79qtYCr8D/NvA/yUif6d89jeAvyoiv40urf8I+Pe/60RTia+pfRvIOP9Gf/idN/m2FXBTe4efkA72UcxaEm2K8w7jQFPXOCMl7VlXPgrKr/6qhjWnfRamff40LKdklRxiQbvVpK4rw72HDwHdKVoJLYH1ejOXTm+aBl9lnPOFAarkIus0JdtZBxIJ4SCCUoqEjGFkmMqlpch2t6VuF3z9ox/x8tUrXr/UDWEr29AW83iqiWidWhC7rqPrdJs4W8KMIYyIyWSzz8Kb7CoNke6thWyslnaby9rr8THFuXS9wRFCR0gJoWA3xbWZxsR5j3OWza6nbhq++sEPuLi4xzAGnK8AYbvd0u+28xxIOZNccU0pJeXKKn6YDbo37xVoVPaoug1t2+Kco+/H2UqYfju8FaJW9qxl2ulKhLJ/pZr3034T1u4jXhN+nQuKqnklxeJDn0HQPJQYI7tux9XVNcZaVquTsstVuf2USqEYwfmKk9Xpe2b/u+1XiT78r7xt22j7hbeKewfog19I4L/Nhfi2794+Lqc9Q1yKD6hkGN2zDxR7mzCQhFq0uUQ0KKQma5UI40rdROULSDEJ8xzhSCXxqut7rLV8+fnnTI9+dXnJZr3m+bMXnN+9Q8qZqq6p6hrnPLvtVsFNwHpH29pS2DOX5Bt1X8RajFO0OhYl0nUdT54+487dOzy4/4Af/dqv87Pqz3n8058RzX4ST+ZsDCO7lLi+viaEQNsuSEl59jlrnkEwcY7Vi9XQn8IBGq7VMUb7qERhZrZeUAJOUzcImTevN4xhRIF+wbpczPxSbLfUkdz1Az/4+mu++OJrRBxjyPT9wHp9TUyZdrksY2bnewlh1B2j/R4Encbn7ZB007ZaEMd72nYxb+A67SGZD8LIk++/dz0FWyIzWudAw4avX78CYNEuaBpNZ09ZAcj9fSguZMXOYzCHzhW6BBKV15od/TiqezkVE57mdNzXmGimAjsf0D4KRuNNYN/h+/cJ9i8i8JPPdwj8ML1/63jvtTpPNgZT4sUT/2BaBQVwJdRojZKEcoaQdA8DDV1NQhURd/x80wRMORND4PTslLsXF/N3wzjy5vINL1694uzuHba7DjGOdqHo9+XlJdvtDoC6qRmHsVBf04wnxJgYB63i5Kxl2S70+axjfXKCFY1yXNy7RxwDb16+IoVAzEn90wPcZtq8NKdM5T1g2W43JBEkaoESN1kW1u5BNilAK7Yg5br0SwESp/7xdc2DTz7h6uqS7ZOBMPYs2qZEfieMQPuwH3pevnzDr/3Gb/L5F18RQsI7Vcjb3Zrtdqu1KkvegYYUhZg1KSrliMcecAbszC6cPlPavVDXmljmvSejgOhhlGHqo5u2eteKyhrFSAVUrqoicpLo+h1IPV+zwqOacHpczZfRMSiVnw5o685aTlYr7K6jqqo5xAlT9EZL+/uCN31o+yiUwk3tlxF4OBb6t0NxUzuyRIqJNYGcbysnFYi8Z8E5LZqZCzIsebJty3kLR8KYfRiLuTKTzJZIKgi8CFRNzemdcxYrLUy663simYt7D7hz7z7PXr7i+nrLJ59+Tt3qRi91MxyEq8xcLfrqSsHJynu6Xcd6fU3bNJyenbMshU8XjYKbU2LTanVCf7ZTlPrVG60FaMx+D4QMzjtWqxVd19H3HVXlNQ2cRBqFMWdGu1cKztW6MsdIVdUgRusQhoiZ06L1/Ltdz2K54u7FfU5Oz3hzdcmLp89wvialgDFCCJF9votldXLKo0efcnJ6ruXrh4HttuP16zd476jbat5By1pl/k3ClqPWjpySujSj0kDcA8n9oNZb27Y0TV0+G0uoeMrc1SgAlN2ujIZjh77HON3FQjd5VctCYL9Jz2bDMI6ktJyB4pyz9itTSPRgzpZFRytrq4WTRWjbFl+3WGvYdf2cXo5oQZ9hHEjkA8r6d7ePVilM7X2r/PsEHvZCf1g6bRL4w/eUz44wDIofPIUkS/bfdIwvvuhhpCiTMYWTZ4zVxJgDU85ajWFraNIhoSRTeSUExRBoT0/ZlRhza7Ucl689fT+wWW+IKVE3in6LMSwWi7JiQ+U91ntiiLx49pzdbsfDhw9Zb9Y8fvyYu+d3qLyG+0AthbZdaCzbWfquw9c1Z+fnDP3AbrtlGMd5YxGxlsY5Fk2D5Mx6vS7+ulK7TYlndmUfh74fuLi40AgGU2ixZJLGiDPCMEZCUbTPXl7yL376uQp4DHzy6HNCiOw2a3KMNE1VDIwJ4/B8dv8T7pxfEMaEtbrPw6s3r9huN9y9d0FV7ZWCMZq+Tcp47+fN29Nkoqc8E7XmsCpQVxXLxYKUMtdXV+x2O7bbLXXdKHEp5XmMU1Qqed/1rNfrGSfzXolPU+WtwxDj5FLGGBiGsaROH5T9L2X6pnl52IwxJSPTYdy+PuiUTj8MAyLMIcvtdvteeXm7fRRKQcNE468s8EyvB5+9LfDlgvPfRyXVync4hzNmpiAPw4ixRSkUEE7/oeZhMiSjiVzWZoiBvt/tTbam1tr/1iLOYsbCsbBCP/ScrE549OiREmLQlW11csbzZ8/5kz/+E2xVcXZ2PsfTAdq2JZS/FbCyDH3PZr3h6ZMn3Lt7lzAENtdralexXW9oi1Kw7QJvLTinZcZyp1mUVctyecJmvWGz3syIUWUtY98jxrBcLOm7nsvLDVXlZgvIOTtnPY7DwGa9xZxYrHeIsVpI1YFz0wY3wovnmh4zxsinn32OVJ7UJU7O7/JgDHzzs5+wux5mE3oiUzVNy8Xd+3hfk1IpzT8GhrHj7M6pbnBTVbP7oAOt0GddOaJBK2YD5IRkIcaRlKKCqsC9sjktWZOQnj17Xli3agFOY3VoWU68hZTSLPxTFe/p2Gk6tosFS2Ooq4r1ZsNud6VZl9YXEFSB3AkcHsYR791sLZnisooRxliYjt7PvInHjx/TNC1ffPkFxhiePH3Ch7aPRimEEN4V+Ok9767yhwL/jqMxMcluEPhDPGFqR9mXxReO5ffj0NP1Pc1Ui08UzY8xl6w81fQi6KTNCVLElVLfoASVmEqmXDE/fVVpCnTO3L9/jwcPHjAOGuHoup6zM0Pfdfz854/54uuvNTV2ol9bq6XcywSY8jKGcSybnGS89ZysVty7c5flYqmm/lhWw1Zoqxq8B7GlSEfP6lQ3Qh2Gnm7oj6oKgcEU8LVtGrpdpdhJyeVYLpZUlUZJup2Wl++HAZ8z1bLSeh8iZTOUXFwpneA/+rVfI4sWMYkJrK9wVU3Trgj9oBu5RN2nAeDk5Jz79x9gCkFpu+t48/qSRbvk4uLevJJP9SNiDFgyTrlvJGOVF5ITGc3CFIGqqmbzvq4r+r7n8vKK7XbDbrfVlPeqnibZEY5gjMzKuaqq4vO7uQz7tHdkPMCmxGjExVpHCIF+GGinoq9ZMzJ3pWjKdr2hbmqtzdk2WO9nUFQKwzfmiC1g7PL0BO+UgOarat5N7EPaR6EU5hXwu1Z4+FaB15djoX9b4CcfcloG9wqnvJvMwZRKJZtBdySu9/RXUNNTCq9/WhWUnKI57u1iOZOXQtKwIaI5EUpcUer22dk5F/fuY42hUIswxvD48WMur64w1tDWNW3bMtFmY4xzlWmYFFXCOced8zt0ux3nZ2e6sc3nnxc25Z4QRpmkzleMo4Jry+UKb9XcXa/XvHrzZqbMIgJBC4pGESrvWSwWDL1aGHVVU1W+oOnKu99ut/R9j681W3QIujEvRkORu27grJQ7/+1/5p8lpKzFVQGMox91h+6YUds8KyAJaLn65QISdMOOq80b1t01jz57xGp1MhODZE64KmSu2TxUayyEOFd6CqUk3GSJZTJd33F1dUXOuYQjdRVPpWDK8dyy2GlXKSjl1nXMNadiX9l7ajEnYs5UVcWjzz7Tkv5eM2mHYSTFNBeSbVfKqpyKy6a8n4vOKd07xkRT6/OcFHwqA2IM7eIvWfQBKAHadwUe3r/Kvy3w0+vMhmSvCKZr3KR05muU60xNXYcBUzIPQTuYAhrlkoRyqCjI+ruqzpgJXY8RgyuZipo2vd52DGPP6dkZi3ZB3w+zcmkXC54+fgYI9+/d5+zsnKZpEBHGYWC9XnN5eclFiVacnJ5SeY93jouLC/q+U8WRdZWwhfU27SKd8gRyal1JYzzL5YraO4a+o2lbxOyrBE3szZgSjCPOe+qqZhz6o7CXKyBZXVVY67i8/CnOeZqmnX1laxwhjXRdz/2HWrXos08/x9eNAmUZnK9Yrk40JXjX8/rNS7q+48svvgTg/qefaQVmQXe7dorjaIl5DRbnvMeNpgHO0/+UYYwRGEJgHEZiirRtOyva3XbHZrOh63Y456nr9iDaNxXYyQfzjv1nRSGIFZz1ODwhjOSMlm9H6cpWPGQFZmujkSG1/pS+nN0+RDr1sU7RPAOP1rmiiHIp+a7HTCHIKZTr/7JZCplMKJjC1G4KUX6XwB9+/m1YwpFCeA+pScHEQ9quvipqf7hdfZ6tDJnqBIov5cT3oUGM5jsYp2XOQ9Rkm8nUXa83LErVHOeE+w8esNzuuHN+lzsXF7ohTNCCHG/evOH5ixezKWqMoSo+cN02nJ+fE3PSHaidIY5BwciyWkUytjxLzvsJl53HlJi2rzxjqYbV9T2tuPn59R4tbduQUip5A57zszNAN6dZrQaePns2P5fux6Cb6wzjwPL0hC9/8EMAxpwJw45YEs+sgdOzc7yrqH3F3XsXPH7883ljlMVigeZ36P3EGFmdnLBYtEU45WgOTLUcpoFMRXGnrEL47PkzLi4uVNEVl+x6fcVms5nDf/scHPXjNaqw3wBGrFHlZARj9q5LmZFYJtC59CPj7H5M4UYtG2/2rkZKB0pB9xAlq5vonCWMAWMdOWs+hHNuxjpmjknUHI9D/sV3tY9CKcDNKzwHrx8CHk7vPygvYjqvmbBzDoRbB8SW/SQVQyhmZhiZir0qW3CagOW8RrDiyiBIuacSZzeWGBPbfkddN/T9wJdffsVqdcLjx09o6rbct+Cd4/TEE2LAWce272YWnPPFVD9wdab7q7xnsVqV/R1rhpJfYb2fV5Fx4gukBFlKpSOh8hVtu+D8TuLu3QtevnwJKEZQeVP2JlRBNKaiqVumPQzHEDCFrNW0S87u3uXHKfEnf/zHXO82LNoWa2AMA32/48e/+Vs8fFQY8aL8jimxKqHg6WK5wErGOcNyuZgn/Ha7JaVE0zQz1+Pi7GzGA6b5QgE+JcVS7i0W4pQWsY2lyM3jnz/WyMs48vpSk4Y2m3UJPepGMuoa6b4MZM3hCGGv+KumwRT3S8ckl+lVFovCLJ0U82GNjoSdlQ1RXYpccmJm8Nkp9kDOVKiFEMZQdqHSNGpf9hzV8ye1gGJks13Pc/FD2u2+D7fttt22o/ZRWApywEX/Nl9/bwXcUGfx7XN+l+XxnfdU8ANRH3UiIg3jiHceIZNiyX8/2H1KCTojq8Viv7u1UPaSdKQccE6R46quOT8/11izMbMf6JxTzV95PJU+a8447zk5PdWc/rrem66l6pK1Fl9XNFkLp4gplotzR/6mFjjV60ipBViV1OTFQum8n3z6GesS2/7mpz8tBK6INVnDd2NkzBHvK9bbDjc2c/1B39Q0iwXnd+7QLFqur9cs2haZCEwp8+iTT1mt1F0aQgQrM9mHsmW8Qh+Gs7MzLi/f8KJYLhPOc3qm4UdjLOfnd452ep5qFYDS0DPKQkwpEcrmryEkXr5+U4rbGH7288dzONx7j3WelDLOOmLSflMzX7knsN+FKs3fq+twmNafUEaH5jPolDDGzi6GhQNL00AI9H1PP/RsyhicnKxoSx7GbBWWfUYFsOheIxNRX61dHec3U7btB7aPQinAYdTgQwX+GEM4/O4Drvat1xCZMIU9MDch3+M4zuSmqSJvilOajZq/Rixj3IOlzleKKI8jVV1z8fAhGXj96vWcNrtcLkstfxAMm/W2cOvVj5zCXMCM8k8DPdNey5ZxTduy3W3ph4FhVBCtIk+Z2RjU5N5ut1hjqesGa4UcA+PQ0W2VNHSnYASvX70kbHa6WUzS5K8YBt1LYRi1ujIWV2L87XKBL2Dhw08e8fr1P1Dswlf7jU7TXtHmlDDOlaStnpySUqZTKTU/Ri6vrtjtunkMcs70w8CTJ09pmpovvvi8lGF3KHsyznMhxkwMSessUujfY2AMidOzc07PzkGU0rzPYdDaFFXlqKqaYRgZS80HrcIcsAfSY4whlkXBoopjn5xUytzJvsjKHnMoLjF7cpzOGc+UFg1aC2IYRioxeFeRUqSq66LgNTqy6zuYMR9PVSWGfuDq8ppxGPnQ9lEpBTgWePjlVvlvE3opo3UTs2zCE6b7UXTezojy9LmuONP3VfGrteiKqrXyPHI8AXJOtIsFX331FV3XY0TDYjElzs/vThsbAbnEvN0+vz9nLq+uuLy64vT0lPOzsyPwaL+desRWmoAUU6LrOl6+eIG1hs+KDz8l9Pz0pz/j9PycH3z9Q9qmJoVAv93w/Olj/vCP/ghXeAd13cAYS8HWsqNR0lWTmLHeqWUyl5xXwanrhs8++5w//ZM/JUQtNosYTQASoa5ViQybLakUSE0pKdjnNLfE+YrXr1+yWp3MOzYDM56wXq/52Tff8Hf+7t/lzvkFjz77VOsupsBUW8AUC2Sz3VG5UuE6K+V4uVrt++4g6pVKXxrny+7dBrGlQnZSJuFhluRkrZXZVyZBqW1p1SowBwlOR3kKYpmKuxpjsCbP+4ROil/vWYFHY6VEkLREn7EwDJBCmmtgOJvJXhez5WLJ881zPrR9HEpB9ptefHf7rlX+WOgPIxTfplRUJxx/b62lKhN3Er9cFMJMQhGNGU87R8/zgXmhKPdgOTs74e7du4XUsuXOxQXWecbdFt+6OYMuxsTpqfIMuq7D+5qu73j65AlPnjzhk08+4bd/+59iURKcrLVcX18jVrhT3yVGKSEvQ7/b8fTxY64u3xB6TaB69PAThr7n+uqSk5MVKUbGUfkY3TAoO3AYdYdr4PzsHMbAm+srSv1bQEVOrMXXDdfrzbzyx7KdXErQNkvadgkYqkrJN8Mw7lmhlLqMomXIpirQ0++tc5zfuaOFUcp1leKtjEVfN2AsL16+4OWrV1Rty8nJEmf39RPHMHB1fU232yGLBZV3nJws8c0C5+xcEHfbdTNYOW3UKyLEkIrLViHsi9iEcc8VkVJjUYzss0VLynwMASMaip5qNMZSq1OPE6zsQUIRtS5ijFSLpoyxUYA160a0SC4MTMu0iLy94fE0R1erFVel1N+HtI9DKczi890CryScX1HgbwhDHhKgpqYZkJpNN5VKyyQqrzHuGPNBUVSZtb+U305xfud94Sdo/P75ixdcX6/5jR//Bm3bMvQ9YdSJCVrNuWlanjx5wutXr7i4e5fl6QnGGZ48/Tnr60s+uX+f+/e0WvH5nXNevXqpXH7vWJ2cYsSwG7bq9uTM5eUVV5c6Me5fXLDdblkulzjnqCtNnLm6esN2u6FuGn74wx/y9OkzPf7BfXbLJd/89M+pKot3BokqMClrfYhuIjqhSk19Ws0KefDwE169fEHXjywWS6qq1s1a+n2p55wz3W6H9x7vqzl1fegHNptrdt1uPr8xlpQyPnsWywWrkxWLxZKu64gxsV5vaJt2NsVjLPH/wjcxxQLbbDeMg5KqQtBkp6ni9VSYdapurb67maM/IoZcp3d8dVUOMkcXrLWEgm95v8cD4hjJqGKYysRLKslZoq4Uwnw/+wpNcfIQZmtW9ataEVNI9TAi5ZxjsVi8KxjvaR+JUpiE6Vjgb3p9u727wr+rWN4R+LeUwjuhywnUPKjqPJQiKzmOpLpBjEVKSvVEX5rKqO8rP+vLZFlcX18VwMKwWK1YtEudiGJ0S3Wr4azoE+v1Na9fv+T1qxc4STSLmrZtePjgIeMwcnl5OfMCRAx93/Hk2TPqRatKwRhev35DTJGTk5PCK9iz2lJKPLx/n/O7F7Rty5s3b3j69BnDMLBcnvDgYTMnSX766FNiHPmDv/37dOOIr1rlahhDjrn0jW52o/ej9GNrNeX6qy+/ZrvZsl6vuXdxj67fzUIIWrrcu5rtriOEwGKxUHJRie8b0SSomXZdsBa1QPR6dwpPI8RI3/UMQz8r5d12Qz8GnLWl3sKaq6sr3lyvSSmzWCw4PTvl7OxsFh5BaBqtX7Hb9OTMvE8FEyBKmtIxirWYVQPZErzNEMuGvRllyB7m9RhrEAtdwUrqpimCr0rJO0+cS7hr1uaUWBWjgqbDMBS25LtWLjC7ZGdlrnxI+yiUwpRRdvN38G2WxE0r/KHQv0/gv+t+CvQ9E3vm/IKgMW7vXTENU6mRZ4vJrthCJGMLw69q/1/23iTGtiTN8/qZ2Rnv7H7d3xzDi8zIzKrK6hq6u4pWb0AtWmIDYgGCBSBANBt2LGhYsKA3LQRiyYIVG1C3hEoghBAIiUU3qFVFl+iqUg1dmRWR8ebnw53PbMbiMzv3uL/3Il5lBqkoKUx6z6/fe/2cc++xz+wb/t//n1O3Hdv1BusU8/mCh/cfYDsnCSAlPZahJr3Zbnjx4gXjPGexOGU0FvCPq2pOl0tiY0iSmKqSrsQ0SVjMF/yT3/89slHOg0ePcM6yXq+JIsNkMiGKhCgEJCkWWIQALi8uePH8Oeu15CumsylN03ByIjH8KM8pS8fdhw9YrVdUdSXybnWN1qIifbI4ITLHXTZJUpQSEtOFgvO7d3j25AuarqEsS4qiYDQVNqDIRKR5zma7pW5q+fsolruepGR5xng2u5FfiaKIPM8pS1lI+mRrnEhFZOv6zsD94cDhUJAkwnUZRRH5aJ2B7VwAACAASURBVETVdJRVRdPU4Bx5mvU7s1aaNE5EiSluUSiMSXFWCHcVkt3vOTutkNfUVe35HxNm8wV5nlE3Da217Pf7fnNL01TAY51lt9sJXDzLqOuatu28p8ItT8RR+/bt3W7PF1/8hPF4wqNHj6QPZCBIo31D32azYb1e38RwfMX4RiwKkuB707YhPPf2Xf6mwbvB+7/kTOoW/JVbXkgoafUYdu8m92dVFGVB26Wk3mPAdnRtgwJin1QasvKAomoaP+kbkjTzYiNSCvPiZ/1Oa0xMWVWyu49G3H/0IUmScL3bEycxZydLZpNJbyRRFHHnzh2cg9X1SiZW0/Lq1SvA8f1Pv8v9+/c5XXpEYJ7RNi0vXrxAvXrtE4OKew8e4Rx89pNnjPOMs1MJT7IkoalLZvMF+8NB0nf2KEMmzWF6kCgFbVQf50Ym53R5wrOnX7DbSQhz2O345LufAtB0ErNro1GtgH2iOCI2kW/LdqCPUPO2T+hJWc468UiUFtxg1UrCbuyZlyTX42iamqbpRIsxzThfJpRNzXazpS5Kdttdv8MmSUyepHQosiSm66T1vbFy5l4/2E+4tuvYrFfstnucdYwnY+IkpWlqLHidz2EDlcyr2n8WE0mXqzGG1WrF82fPAFieyz04OVn0nsL19YrLy0s+//xz7t27z8nJwne56hsJe2mqqnn58iUPHjzgfcc3ZFFwfS3+nTv8e5Qq4c0w420G/8bZnQsux82wwspOZ5IYKn9Du4jWdri2RamaOJJ8QWuFuKOzHcZ5zgW/cmujeuGY9WbLZLViu9v2CkVSteiIjCwKi8WCqq5wSKlyfrqkKIs+TizLgvlsRuI1CppGkGvT2ZTZbMp2s2W33/eIuKurFdvtpm/DzZKY5y9e8Kd/+k+ZTefcu3Ofhx9+xGq7Z7vf86Mf/xijFH/tN39DrieZMB6NyUcjMJpqX0llwAjOwSj5bH3exXXoSBEnkc/Ix8xmM5IkYbW6ZjqdcHn1ut+9rM8tBL4M4ynxhdlZiY6GZ5gGyDyiVCnBLFRVjTVdjzCMIkOWznvCEWsto/GY60thce5iSxyn5FlCbkUfs6kbiv2+Z4yWz6aYTafoRJKWtu08DsJRVY1vlZZrD4IyIjYjmp/7/Z6maUizjMlsemO3bpqGPM/Js4y2ndC10iintWa/33F5een7T3wOoms8pkWSyp999lmvW7HfHxiPp32Sdjj327Zls9nw+PHjd9rM7fF16D58BmyR7a51zv0VpdQp8PeAjxHy1n/VfZkgjLvtJr3zXO/+fQBzvnHoQZb7xmvvscBEkYimqq7rCUp2TdN3qZWVxJp5nqN93d1a68VQLUcGtoBjV7imY71esdtshROhjciyMa1zvQDsPE3Z7nfUTUM+mrDeHbi+XrHd7dltVhS7gySoPIHGZDJhXxx48sUTxqMxv/8Hv88oH7FYLPwiseZwOHB5cQGAUWfs9zs2my2x13fouo7tbg9KU1U1V5eX/Oif/kje/+ljFrMJ08mENEnZdh1YK5Pcx7+us3ShFm6dJww1dF0DOMaTEXfunfPq8iVFdaBtq748N8pzDkXhiUGEQCaOIiG0QfAijXU3dsGubT0gqSMyGquE69Fh0SoQ1srlGG1o64amrgHlGaeMaDkQMZ/NqapKeh38wrOttuy2W7bzOaPJmCSTTtUkSXCV5VDX1HXFfi8hyuGwR2uhqG+blt12x8nylOl0SpImIjg8SP4VRSFaF0EyzlcgmrYhTVI++OADnj57ysXrC/+VSkl3Npv2jFAhuTidTo+MTQPoexhxfOxgfZ/xdXkK/5xz7mLw+98G/g/n3N9VSv1t//t/9GUH+FKDh7dXDN5l8G994kuGc/0OcPRYHFol/fHDChxcuM5aXCvdkiEXoL2b2rWtcDX6XaRrEuq6oPPAJ0mCdR4pZ9kedhyKghPf8FPXFdvdjqKquVxtMFFKUTckBra7HbYR0tCgYxhFEafLJUVR8OTJE2YnJyxPl4xGI7Isp/O6BpdXUvJME9/377UNu65lu9lw5845H3/yXXQU83//g3/IH//JP/X3wvKL3/9UkJxKS8MPDqOkdBhHEc5aqrL036egKR1en1IJ4/VHjz9ktb7kyZMnKGB32AEwX5xxvV5THA6c3bnDZDKSSe1VvdEGnPRpyK2VYwpn5FFZTCkxLIu7UZ6LItNXQINsn3w3KV3X4nxycbORJiiQPEQURRyKgvF0QpTE/vvSzBdzosgINsCfv21bptMpGkNTSQNSKGNGUYyOPGeCzwMdDgfqKCJPRbg3SzOiTO7L2dkZeZ5zOBS9vqVztg+bkiTm3r27VFXFeDzp80PDLuKA3gS4e/fuN6L68C8B/6x//N8C/ydfsSjcHn348FPu8oMD3TD44UIihxqEJcOFxzlqGg9RVn13W5omguKrKlDKZ7tLjPHszU74HLVWRH4hCXqE2kjNu2ta1qsVy+USrTWfP3vJdrvne9+TGHt/2FGU0v683hfoqCIdTzkUB6bTGRcvX1KWJaFwf319zcnpKedn5yitmM2mzOcL8jyXzkHnuDw95epKYMKdT+aNJ1KSrMqK7WbDJ5/+gO9+91PSbMzzZy/5vd/9bQCe/OQL7p+dMhuPmU6nNEVB59ucjTZoJ9Rggb4t0qJqZRGmZCnRwXQ+56OPHvPixWtJbr4UQM1ofsaryysOVY2OFEmeYOuO1tbCh2kFKDzc/YYGEOLnkMPoulZQhH5jiZOEyXhM20iCrvVqzqF0aowhyzIeffBBX+FoW9GZvLq6YnV9DVpTVRVt23Dv3j3u3LlzgydhOp0yHgk+Qk01SRSBUX3lKfY8j8E41+s16/Was1NRht53+777MfGCwtqfE6CoDmRZ6pvRtH/dMB6PiGNZTJpmmOc5ol1ns9mfA/j39TREOeB/U0r9P0qk4ADuOhGgxf+8c/uPlFJ/Syn1O0qp32n/HLjsb8e349vx/+/4OjyFv+6ce6aUugP870qpP3qfP3ID2bhxlrqjZ/DnOPNtt5+hJxAaqAbvHfzdjeH7ppUKeaYAihL0YhTFvgFGWIVQWhiUsNi2pSgrEr9aY8HECh3HJFEgHYmZjHLauma1WWNby+r6it32nOXZHfIspW5bDgfvum63XF68om4afuXXf4MPPv4O+6rmj37/dynWV4xGI4/6k8W0LKWt+tNPv8t6s+Hk9LRH6gUo73J51oOXdrsdeZaRZTnr6xV0lij2u2krsWuWj7jnM9ZJpHj29Cm//pu/SacNf+Lg4sVztBVeBoe0lAcdQwm9DF5KllC+bxvHnTsP+OSTTwFN7ennmtZSNS0X11d859OPMNrQdKUka43yer6qDxWGyeiefWoI8tEGh+13/cPh4JNyDdPplDRO+ueyVCovcRyTj0a9UnTrBWi3m01PZb/b7SjLgizLZcf3XgbAZDolTVKyJMW2VkqrSJlUAFCu5/wECTkDzkBpzdWrS1arFSaKuHf3Lud3zpnOpuSNYEvSSt4fWK0731od5O2DsvkwNxfHMdPptKeof9/xMy8Kzrln/ucrpdRvAb8BvFRK3XfOPVciI/fqqw/0xoHD8d/i+n+JwcNNo3+Xwd/ypm6DoBQyMbrOoq0ZNKaI66amiqooKJpWUGadsOkEVmfFkS68aQTWmyQRSZxQVhW7regTLJ1jPBqRZjm77RaA69UVT37yOSfLU37zN/4y9z94TGUVF6+e88VmJQm+wbWu12varuXRw4dEvpSWZbl3NQ1xEnF2dsZuJzH86vpSUIhFyaE4ECvN9eUlu+2Wpqp48pMvePXiOV6ciM5adocCDz3EKEOkDRrru/Ms1rbsvSJTUZZMkfge5dW2URRFycl8wS/90g+5f+8e+VQANftDwdXVJc+ePSNJflPO2XVop728uwcCBUyAOxrYbZBO0zQkWYo2ps8PrFdrLi8vKQ4HZvM5sZHqUFD2juOYPMvQSjH2NGYg/RWRMcLiXFeUZUHTtOS5cGEkSYKOZDGKvVx90zTUVYXtpC8BdVxkpIQq+aeTk5M+Qds2DZeXlzx5+rTXgJxOp+R53oP5RvkY6zo2my1d13F1dUXXdcznM0+WMxCN8SNgOYZIx/cZP6vA7BjQzrmtf/w3gf8M+J+Afwv4u/7n//hlxwnErW81eHnD23d5n1xCD+OgN43+JgAKbiMVetTk8G1KEd14b1ikLEoZppMpsYnIEhFicc5hotgj8LQIxXSDktgoJ80ymk4SS3VVUh0K0jhilGfoJOFQiNFuVtco19GUBX/pL/0yOspYHQrG02nf9ltVB7T/KrbbLc7RMxxZHOPxCK0NURwxNlPG+ajfRcrDnqLYUx4KJtkInOLJk6f8zj/6R1RVw+/9v/+EZ5//GYv5FIAojTBRzGQ8w3BJbCIiJZ2Arm0Bid8PpV8U9nuaqu7h3VVT0vhsfbSMODk5YbFY+JZleHW95ep6xfX1iqqsqcsSjTRZWGuliWqwC4b5Ev5Fvp247xRFqgBhd1RSjiBOEpy11G2DblvKqqTd1mR5TuqJcHvFJyVclOd37qCA2srxr66uKKsSZ12fwAzzAmQHbxpZcIWkV9qXl+fnpFl6I2E9m81oakE53rt3jziOuV5dC2CqaaR1Pjp+BtfJOUUYV1MUe9brDYvFyQ20ahjGg9xwTohq33P8rJ7CXeC3vFFFwH/nnPtflVK/Dfx9pdS/C/wE+Fe+7CB988btHZ6bRh8Yj77M4OGm0b/L4G+d5sbTch7AeYUfbI9oRKteVzBLEyJtBMK738mxBmFMFPrllTAgRx6qq5Huvs12jXOWpqmJjaHwO21VHDhdzKm7hqo8YGLHbl+w3x9Yr1ckWpz2wOl/dnbG2XJJlEQ4R6+UnGcZaEWHpXWO+ekCgPnpCVx0rK2jq2vyyZT5ZMY//p3f5tmTpzx7/gzbVuAkKVZVLY8+/CFpmpDEad8NKfAO4RtIkuT4HTlH29TQ5T2pbNu0vcFkaY4yimfPvwDgd//gD6nrmsViwdXVNZMs52x5CkjvgzFRr6wc7qkbeAuBiyNodqJVz8wEMJvNqAsJR5IkwWjNYX8QjYb9lsPhwIlnXhpycAA9C3LnhNY9iMOIC98e27nrRqoeHvJdVRV1U4Oip9mrqorVaiXTSGsR9UlTJpMJzlraru0RiCEJ3NOr+Ta71GMgTk9PUUp5LYo9p6cnfXv2cEiS1/78xGCccz8GfuUtz18Cf+N9jyOEnm8avLwG79rl32rwxz96t8EjHXngSzfO60g66wFLQrmljZEJ4Cfa8Vpj6cJURkRVrONQSMkRP3mHhDHS9WalfBlFXrHIcX15SVEcqIo9HY7OlzD32zXYBtfU/A9//+/xa3/1n+Hp6zU/+bM/o61rX6NGeimA+XxG5DkQx+MR+0NBXdVkcUqS5MRGUTaaua9lL05O2F5f0zUNqY7BwgePHgLwJ3/8h3S24fz8jmTQgaatOJnPpdXZf9lRHNxlizPaMxz770j7fd7zWdpGvECjI5q2I8sNu92eV74Gv9qs0MoIAtFa9rsdJ/MZeZZTHAqUEjfbxEfB3tAMFNziJEkIbe3OWoq67jkEoiji7M45kToCoIwxFGVBURaUZcluv5d2dV8dcNb2tOuHw4GtL5+iFPPZzMOUuz5v0fiKTgDBRZ5QtW4qOU5ZYOL4Rp7DdpbFYsEozxnlI86WZ9RVRVnVxHHSVzZAwlYdx8RxTNt2PHjwgDRNuby8lPZ73xMRqmTOObSTln/tNHn2Fw7mDMqotxq8PB6+8d0GL88LYYX1E+RdBt8vEr7dVeH59IxGq9iXIQ1pkoI5NmppHdG2QTZM2n6VVqRpSuW8LJpHloW6sgBpWtrOECexT0oq1psN2+2WKI6lTOh3qa7rpE9BGf6vf/gP+OzzL1jvSq6vLpnGEaoRRGHhF5HEGHQkC9H85BT0iqA16GxH50RvMpQMZ4sFbdd5tF1MXZUYYHmy4Pp6jjaa8/Mzam9wzmrxYpzEuxbnE2kizFJ3tbjeARpuNK7t5B/QVDWxib3orEB7nz5/zpXfNU+X5xyKPcp2jEYS05dFSRIloDy7kR4QsfqF1znXhwhDJF/bttRd3ecU6romjRNOTk9wDtqmRTmh5I+jSLw85VuifTKzaVv2ux1lVfnQVryG69U1RVEwm85I06S/x00jRLyJiT2TtgCr1usN4Fie3+HsbIzNvSxdVbFerYijiMOhEHRjnnN2LoW6xWyGieMeJSpsToqyLDxHpeP09JQkSRiPRwgJrIIghKxE7VtYoTtc+/6uwjdiUVDKt4i+xeDD6/L8lxs8HJN7iqPGw22DD/JcgG8k0f4509fVQyRjrbD+2uMTcr0+nuy83sJsOmVtO3bVlrbRPp4/9k/IkJhQ4l5xv/eHPcuzc4q66gkyJtMF2+2WNBuzWq35w6vf60EtsySiPhR0OLSPVaq6oK5LJtmCxWIuxpIkjPKc1jnqskBHkl8AqakLVZfwTlR1xatXLzBxwkcfPiLPUqI0RiGG9uPPfszTLz7nF3/hB6R5SpyktNZKY1AsMXycxL2BRnHs4/2OOI5oO2EdzhJJkJZNw6vLKy48mOrswQN2+x3LxQmTyYSuKtjvdqRJKr0PSvlF+OjdBU9h+E/miie3jY4kqk3T0HbtMTpVoo05m8/IspRFNRc+hcOB+UJCrAA0CoAs5zeJyXjSw6e1Nn0/xna74VAURCYS9WylSbKMxcmJiLx6hOZ4JAC1yWgscOgoFnSmtR6ZmPRQbYCmPrIyd03DbrfjxYuX5HnGcnnGYjE/9ue4I45Dunitb9rrbiyaXzW+EYsCyKJw2+CBgdFLeVDCfW/syjMMalkVQVbIYPS9wRshwLht8HCM1Xoko7W0oQ/CedEXBYHLzNoW5+R8xhiUAaMEuGR2EtMrJe3Ageil6wJmXhpjJtMJ692Wsm5YrwR4FJuop1Q/u3NPknDWMZ1OhV8hTZnmOdV+S1GVpGl2nPR1S1mWTBeKNEpJM2kScvjUCI7rq8vetazKkqKUHdBpTZZl1E1NjGM2nTGbTYmTmNaDYYwS4drWc0+enJ5wPZtR7HZYT3UeSnggO3NdV+R5hqSKHHEUkeU5jec72BUHnGdbPhQFq82GX/z+p0xnM1avC9q6oW1qtG/Wss72yeSbeguSPxg2sAUFrYU38MViQVs3XPudOU3THiosCcmo94oCe5IwSRmUL/U5pIKQ5zlpkpLmGWmSUhbC8xBFCevViv1eyGZGo4zJdMrZ+Zm0YefZTU9YK87P79A2DVVVSUJ4Ioli7QFwMvf8HGoayrrk+fMXfPbZn/Hw4SNOT09pmm6gPp73TXVd29G1tg+D9Ps7Ct+MRcFay3a/8ymFo8EDA6OPvcEPdnVv8EbpPvk1NPqvMvjhe+BmOQf/rITQRxJOgdiCMYK+1WiMEkKRKDESWypNnh9dWpH0sri2kdq2x6Kv2XJ1teLhBzVKG/Yep/DdT75D13U8e/KEyWjMbDIF62gqaX7p2pZ8PEa1PilmjNT81WDa2U6a+KwY08Wriz4H0TU1h8NeOAWdJVGSuFQIPFlrxXJ5yovnoj/YtDVlqYW5yKSMx1NG4wnlbue/D0VdVaw3Qo+uvJp6VdUoVXuCGYmxy7ri5cVrDlXJaCElyaqpKYuSO3fuigL2do12eCYjyexbXC8Mq315eohwDDE1yMJg4uh4z5yDGM+c5MudKlCvOybTKVGSCEbBe3VRHJP6jH5RVvJ3rWU6zpn7PABK9dWBLPfycFpRHAq6rqMoCpxPeEaBys1/hshGRCZifzjw2Wefsdlu+eTxYyG79dyLDtdXFUwUUTUli8Wcs7MzX2aU5O1+vyNNEy+64z+zUtJ7K/jvPpR6n/GNWBSUUuRZ2vPP6QE1lTYao3XPaOOCUeN/DgweuGH0X2XwX3Y9AUvvnM9D9NRZEJlYcA9agRM1ojiKeu3AJE6IBmKgAqcFvKBLU9W+jwCK4iAts2nKyoOL5osFH3zwiKooMFoLCKrrKMsKO5mx3+/Z7go2vg350x/8Akkq2f/OOSIfOhmvPWE7S9e2XHk25PV6JbGuT3gSIfwQkTTkrNdrJtMp46mUJJumJUkdq82axdldFiciNnMoCkZJIsClpiHpZJcd5SNpM27q/n5pLUqQrbM8f/WSqu2Y+p18+/oV2TgXvQpjmM/ntGUlTVYIaYmztk8OH722YxgR7m9wpaNBOKOUwsbW8yt23uCOEm5NXZOkGWmW98cxdS3KUElKmlW9cKzSgtVQWolRB8GcoqQsSuq67vkdd7sdr16+lPLjfMZsNufkRD7zZDIl8biK0+WpCPB48eKmbSmLwvNWBHi9JArH4zFnZ+cAXoTHMBqNCUSzfdmWEE47X/X5C0bcaiLjY2HoP04IH/zjm7v8cVGQl9808bcaffBEAr4Bn6/o49Lj+0Jmw/pYtk80KpFakzZfK0AaS39TemSddUe6bRx4NxRle5KWyEgX4evXF9z/6ENaj1D8gz/6Q5Io5uPHj6mrihhNXZasWVMeDuwPhSdeEVfx0QcfMp3O5Bps25fVyqokixOK/YEkSnrV6SJOmEwmFLsdaIWJYjrrmI7GdJ1QoB/KgqkHFz3+5LvoSPHi5StGs1NGozHT2YzPmprYCHt0fXHJPb+ImCgSNmUdgZLfTZxwaBs+f/IFu6oinU3ZePHUqmu5e/8+SZqIcI02WG18UvgYL2vnAwhzJKod4hc6D9aK4gjTGXHnoA8vkjTxpb/uRsNUnCTC0uRc36oc/k5o9eS5QHaz2+/Z7XZ+w5BjFMWBy8tLmraVBjEnpDkSqpU0rbRnhzkW+TJr3TSMcmG/zjzQyHnC3YuBCthsOiPylPNZlpEk0mxlrSRbA5FsLzqM8Z/Rz8WBV/VV41sxmG/Ht+PbcWN8IzwFHLRtx9u8AHhP139QoQiewFd5ATf//nYJ1AOPtIQivfCKUmij+93GZyMpaslWp2kqO52zpH4nt+q4+zgnYJ+6roli2VFXq2sefPRhn/m+vLrCOLh/5y5ZljEfTbi8uCBJUqbzBXVrOT0/672djz76WLQGjfA/tN2RLq7tOp4+fYqJor4121pLW1es/PV0PntysjzjxYuX7DY7nDZMF/L+73zvuzjg6ZOnFGWBjqUUaa1AnHf7A9//3vf4lV/7teN3aBStbcmzEXGWEecpqyeXvLp4jfUgop3vAByPx5yfn8tOV9fQdkRJ7L0NSSbTdTQ+PInzXMrKKJSDtm58CbijKvc4HMuzsz58OJaGNWiN4ZigBMFVKA+EGrJHFWXJZr0WpmylePToESryUoLOUh7KXqyl6zqyLCN1IuKTJEkPVd5uNhJWJkmfbLROmKCEk2HPdrPBLk85PTnFOcf+sGez2fTsUXoh8PogRhN7qjgl0UxPRxcYta0PJYKS9WQyfXPOv2N8IxYFh/M8/V8S66v3MHj/vncgoAYP1btefvPagjpp/55jIrPzEunKBviz6htg4igm8e56WRc9PDq4vELUGlFWFVeXKw77AxOPu7+uaoriwPVqxcOHD6nrmuurK+qqYnl+hzv37rM8v9OTsiyXSyyOJI4p6kpgz9ZxcnJKVVQ8e/IUcHz6g+8BEOk7FPsdXSs8CnXbcu/OPe7ee8DV9ZqXn39G0db84Jd+CMD5vbuUZYmKY6bzGVUjbFR1U5MYg+0aPn78MfcfiK5EUYvK8Xaz6ZGIWhmur1dEOiOTm4fzNXUM3DtfCku0crQ4oijpsSDOL8BBkEc4DOVPA7dh45mYq7qiOBSMJ5O+TTkYjNzPAChT/cLwNiHhzrNWpVnG64sLisNBgEZjKSVGRhizKs93UJUlUSycC0maEkeySKdpgptMaJqGLMuOlHta03pIfF3X7A4HIo+YjKKIOIo5OTnpCVdnszkoYWCSkqvoT1o7CFmdxXnn33VSwTP+c/6FVIhCvWnw8vRX7PLqbQb+DuDTVw3XA9L630FuoN+vfLeeeDO2s77fyif0fBkpjgX8FHYopZC8gwR4/UTuWstmt0XrjsuLCx5/X4y2Ho+pioLr1TXLk1MOXccXT5+yPBUS1/F0zp07d8hGkpm2QNv62NpJLwQOqkqSX9vdlufPnzPzvQwfffQReT6i61qyLGM2n/PDX/ghn3z8mA8+fsymKGi7hpknbh1PZtSdZbY4oWk7Li+uaduWsqqJtOHjDx4ym8+ofJdk6xy0tYjGGoNzmt2u4Opiw3ZdkE5GbDdbrJX4t25K7p6dkBotACutqNtaJrlzgtbUJujF3sS1hF4CpanbhrPTM9SZ5A+GEnChPyLsnqFaobUI52pjpNrh507rd2TpGyhYrddsthvyPKdpW5qDwLLPfdIvKFYLN+QIhZKGJsQgrU8gBtxDmSZopcnyjOXZkiRNSdOE8WhMkiTMZvMjbJwwDxVax325PSiqhYqDiB2H70gL3Lxt/2ImGpVSROmbNNX+xVsPv16D/7IqRBhd51DKqw2FhKFzBCSAZP2Fwts5J+U3dwTbaKXxFVPBAyQpWZrirGNSjllttjx/8pRf+KHszFWSkEQx+3InMOiy5MWrlzx89JD5Yk4ymmJRNH7nrA4H38wDygjuvjgULOZz6koSWdPJhJ989jkASZTwyeNPePL0J6R5zunpkvsPHhAlKePpjB/+8q/w/OVzjO+taDuL1galBKF3vbpks9lIHd8YHn78MUk+Fk1IwBlFazt0bHBKrvPiakdR1HSdLJBaa+mPQNB9s8kEozRFWaL9DuisA+ew4or1QCGFOiJT/XxI85Rm1xClQsraDZK+IcwynhJNDXZPuZcyEZQ+lpEjv9MPVaBCdWE0HuOsZbvbCuIVODs/pyikFBmZCKW1lCR9b0gXdTR10+Mhttutb2lOOD+fcH52LmxNvmnLOuG8DNcusGn6pKc8rzFGNiTXA5eOjXvaCBlsZ7teR/R9xjdiUQB8xvxnNHj/+I3ngA9jgAAAIABJREFUfprrGfzftM1Rj6I7hjF4cFPdiSBqkHJPEoHn9jGr8WWuoH5kHdbrIaZZitrsub5e47xRxSZmMh5j207q03VNYx3z01PiPKPrLEVZ8tpzLl5cXnqy15qOju1my3a75cH9+yRJyr1795jPZj2113a35fs/+D4Oy/awJ84yqq7DVBVlVXH3wX2IDTrE5B4n0Hr6uTSJGY1yPn78EZE2nN+5I5DcHoYseYvFYkFVtZg05eWrV9RtSzYZ4SLBCAQlpU8/ekxT1xRVBZ3zwC/dk5k6L+QbJQNSUoKe5/Eux1HsYdFOFuKBIQRo9O2mqlCFCG35wwUkz3Nmc/HK2la4KcpKFoY0yzh4olYA5fMjxjc6ZVlGludst1suXr/GOeH0tL7pbTyeYHwjmSxOXd9U5oyGDiz2KKEoIJ4j9sUJwW0PburEU9W+dTbCEEet56X889nTN2NRUEew0lvHW3b5r8vgwwF7vMPgH1a8AWuPu0XbNgJ6iUwvNqIcKKekPTpJhOSjO5aBHA6nnAe7iIJxExJbViTB2rbjid/JjYmkrbasSOKExfKUX/+rv87j739K09a8fn7FYnHKT74QGvDV9pqT5SlXq2uSWKjF2qYhThKyNOPevXtUVcne8zVcXV1hO8vy9JzWabSKhIi2rmhdRzrKObt3FxX7kp6S0izOEZuINIlJkoiHjx6RxYm4zl3Xo+YiI6Q0Qg+muF6veHb1GsYJ2WIusOyy5cSXPH/p00+lzr/fMxmNj+3QtvP3QIlEWtBtNBrlHJERMRqlNc463ygmbnpkzBtzatg7EWjYxK1+S2lbySKfpSmL+UIIWpViPBpjtKauKhaLBYVHNLZtS9u1fciYZRk4yfPkI9GnKIuif79WmiSOydIU62qqspQFYtIJX2MU+c/lE4e+NVxHQjoT5qga5EOUB2SFOadDLqVP5L/f+GYsCsDQ+/lZDB5uGv1R7PNNg+9f/4ph9EAMpnNAi3a2h0YpQDvBL6RZSlu3ctzBnFQIdDaOIrSRLHjg4mvaBme3/PEf/CEg2fhf/yt/mbt37rBcnhKlKfdtRz7K2V8WHMoDWZFzeSkch9vtms1qxWp1xd27d+XvTk9FVl5pFqdzmirr+R24uqapO6ajMVk2Zr8viJMtp4s5WhtMZMh01ifgJCY+AoWyJCVNEuKZZjqayI6vFLnPlLe2o+pa4UZoLT/6/HN0FqOiiFp1NKqjqAqWZ0sAFqMpTXHgsD+IhJqVHbFtWkEZOhFKDbDrJM2I0wSljtl8/PdL3zfg3ppcu13JiqJIPD5rseqYaIwiI0zRccxkOuH05IT9fk9ZlUynU8FemKPqVsAs1FVFkqYkaeKFXVrG4wnloSBNU7Y7WZjLsmQ6m/r56SiKosc/BKBTEsd9zqTtWuq2ITUJWktDXeCVUB7UF1rMAWwQvclznOv+AiYa3fFmfNX4MoMH3tjl3+v0LlyDu/Gkc8FNAwJwBtVfr3W293IcAWor2WSD7lt9nbMoQ4/I00bTeXWlyESMJxPatqXYeTBPXXPx+oLHn3zCdDYnzjKiVEKS1WbD1eqS+WzOn/7pnwDw8OF9DtsNq8tLlicnnM4fYpXzvSIdSidEcdIL0nad5VAcWCxPpUrw8iWH4kBkFIv5QmTgB7ts2za4TmJ0CSEUximSJJVFrWmEd2DgesdKUdOxulqzOxxgNMKMUrZ1gTGKwldkACKtKOqa4nAg88cE2R0Pux3aGGx7nNiddZgownbWG7XccynDCtRcyE2Oyeoh0WufF+IobGy7Tvo0AnJVSTiiMvEouqUsEMXhQOW9hM7avsJkPTv3dCoU7JGJ+pbmsqpQxjCdzXj+/DkgIdz5HUlSKq1pu5bVai2NZWkiQKnI9N6RxmCgTzCGnEj4LIG4lqAfaSLatvGLhv750rF9neMNg5cHvYF/3QYvD48IyhBXBg9A3uRjVH3McltczxasjUYpjbKA0z3TjtZD91XiwTjEuM6SJQmgaDtLGidkSUIdSRKqrGourq950HY4FVFULbNsROeEhmtfHGhdzWwuJcyz5RJnBWsfGUMSR+g4oqxr4Q9QUglRnmkjzzNB9jUNGqEN+/yLL9iuV/zGb/ymqBU1bT8hYxOxqir2my3j0QijlU+oCaV927XSOek5F1UkTE3l/sDzly+J0pTCCQK0bApGoxF5ljGZiGeRRjEHpegakUSbTCZ9y/ChKDBK+kGT/h4qqiQWfIGvIESeZ6DraoGgK9278r1YrBpyXBwXveBiO+sEVg19D44xhshagW5rI7RtRveLTmh3T2Phmkg8OtL6blqlRVGsqiqatqHwKE6Lo/ULhjGiBJaEXESW9a3o/T2IY8+4NMhVDT7DDS/YD+uOIcUxEfnV4xuyKHj1HRuM8mcweP9C2OW/3OAhGKw8Uii0TCpUj/cMkuThsXZCIx6CBylLKt/NKTdQq4GElxH2phAYaaV8zCekGEYr2jzvgSdN13IoDux2e4qiJEolZnfWsT8UNG1NZzseemKU0+UpTVEymUyYzWayUyfS179arbi+vKKp6h4/nyQxznXEkSFLU+bzOZeXV1y8vmC9XnO6XKKc6/kXcI7tZsMXn/+Exx9/zGQ8wtqOrmslWZYn5KMRTZ9YjbFG0zm4Wq8Zz+c4YyibsldOWs6mnPgavO1a6fFIU58kRDLwncVozX4v1HNb3xuC0SybMxangMt7ynNJFB6rE8GzsNZ60M8AgKb1GxuF/PDut3N+YZDSpzaSAxiNpMxY1hXKKepWFnLjiXs7J8lBrYUWL0lTptMpzjlpD19KyCQMThmh1TswKsVxjLWWzWbT5x9A2KMCkCl8pvBZIq8pMSSGbX3ysqe9/3PAnL8Ri4Jz7hjv9s+FB+9264cGP3xPH1a81eDVW9pIB/Xd3l9Rt98BeGSjMjhn6ZzPjivV19Stc4JHUK7Xn0yMwTnVC6FKt1zn3UDxIBazed+zsN5tKauSQyklrkRJ1rxqKq4urwDLYr4geSy7yMvnL1ienPLBBx9wcnJC17R989Xq6pqnT59Ql7UwJwGutRSHA/fvPySOIxbzGaenJzx/VrC6vuL09BSjzVHxuGmoypKXL19w/95dxnlGWRZitEvTL4KxZ3ayWoMxNJ3FaoXVGpPEtHRESlrb75yesJwJbqLY76iriqZuSFPJZVhnMUYzHvkqjDZ9a3bVNhwOBxKv0hxHMWVZEcXSMm0725Pl+inUg3yGnZTB7b6hJ9F7qL4S4UuSkYlEz8N7HXGaUFVVf01BBi6EPqGc6ZzraeGiSAh0QTaOkBiFI5gqXKMx8nkD2a5SqidhHeYHhlWVG4rWyqDUUQE1eDTvM37qRUEp9X1EGi6MT4D/FFgA/x7w2j//nzjn/pef9jzfjm/Ht+PnO37qRcE598fArwIopQzwFPgt4N8G/ivn3H/x/gcD27k3vQA50Q1P4LYXcNzXe1//licQvIDj3v+m7vQ7xqBEfOxTd30LreqUeAUDyiyjj9DrQJEu4sQWbSRs6LpWdBF8bO6cdBIG9z42EW3TsLq6YrNZEycJkVIcmprddkM+ytGKXoHqxYuXghdYLtltt5R1zenytN8J69qTiPqd/7Dbsz/s+c7VJdPFnCxNOVnM2azXrFfrvvxlO/kAdS10a7vtDjrJpxRFIR2kOOm3cBbnTP95n794xR/96EfM7t+nVdBhmU3nXF+8YpqmnM1n4HsZlI6JjKEsJN9gIoOxwvNoO5GPC3kBgPVWBHQd4rkFOLNziezouF6b0U+vPjl3nFY3Q07tw4Q+5NDSYSgoW7wQsKb1XJRZlqG1FqUu6NGMYacPsX9AEqapaFGG3TxAr0NLdiDiCcnD0WiE81WJ4fWG9wxbxocsVEe2aEmEBwHcoUz9V42vK3z4G8CPnHOffyne4B1DJm4ziBmGbr/8H6DEX2Xwb/72jnHzD26AO258hoCiDItCfwNFmjwwTEtYom7UlsNRwt+IQIr1dXjtewK0Z5vqiH3vvHWWrq5Yr6+pioPE90hbbBRFZFlKVVX9dRaHA1eXl1wtTthut9R1xWQyIUoikiQWWTGlhPMQSEzMdrvl+fNnoBWj6RTjY+baVwHiLCPkQKwnDEkTz4LUdZzduUM+ysnGYxovOBsnngMjSXh5dUFlLYmCaJThvB6CUTAfjcmimNIzBk2nCXme+dyMV3zWsjh2TYcZK9qm6XtDHPD66or1WkhdyrJkebYUw9CaKDI+Sx+ag+RzhORdWFyGRuw6K1R7oUXeal9W1n1Lu9aaNDJ9pSk0G4GgMsM9iT2p7ZAmLoQvQ/GaoDcJeO0G5wVfjtT1IQeRpukNoFVYXEJuJCwWIblqnbAv4ehD1vcdX9ei8K8B//3g9/9AKfVvAr8D/IfuLYrTSiTm/hZAEhmJgW6EPSEpJI9/2l3+Sw1++L7jGwa/hCqF67PhwYXRxqCU76d3R3BTWMVxoqP4xmVpLTEvDZGT0prW0hgTh8nqocHXV9dsthvuda2oJjc1k+mE8Vg0HPJEYtUsTWnqmtX1NZ9/9hmdc3z0ySfMF3NRHE4zaqp+0R1PRj24pioLqamXFev1ivPlmUx42/Xe2ma9oSxKlsulULfVDZPphDhN6ZwVjYJMSFoA9mVJWTfMFydcNzW5zskmIy6fP2OkNecnJxjcQLdRFrvJdEocCwW7UpIFij2DdNM2GH/88XTCpjhw+fQZu92OxckJE18KlATlMSkM9NL2wx38Nseju5VTcmowNfyirJXvOeAYxwcjHwrNxnF8hFD7UXsNiJBzGC5M4WdQjGqaRhrQlOoXwrDQyOW4Hq4dcgzhM/XX4xOP4dg/10VBKZUA/yLwH/un/mvg7yDm83eA/xL4d27/nRvIxk3y1EnT0c9m8P563nxf/+YbV+Cv440LAzwrj0+K3V7xw00IIcWx8ugTU1oLx9/gsEmaYFtpXmltJ9yGHBtxeq8DODmZs9sXrLclF69e8/jxJ7RefHQyGtPWlUjF+Wsfe0j0brfrJ+LF69fEcUxxKHyS6bhwKRy7zVpq6EXBh0mC0cK63NRNr5ocMtajPOf09JSdz2S7rkMlStiZcehISEqDCTx/+ZKiLEhGI7QxHIoSo0c0ZQ2jjLPFCc1u7ys4UHctGM14MhFuSCt4j8gDqYqqQ0VRv2Nno5yHDx72fQdRHBNHEUmS9o1P1toe7x95QtzbyTy53YMk9eA+K78hBC0FjXS/BnLU4CUEIw+Z//Bc27ZCGOt37qBgdXsxCI8DEClQ1YfrDOAorXW/iIbz3x7BW5Dr8YrfCOox8HO+z/g6PIV/AfjHzrmX/sJehheUUv8N8D+/z0H6BWFg8HKMwXve2+DhuMsffw8gQ6WOzNFDo+93jdvVjGFOw3oufSXNKP2KrQZwOF+NCMhS5WG6bSsAlzRNhSHa51HwsurBCOfTKV3bcdgVXLx+RV3V7HZb2Smd5cXz53zno+8wHUv2/uTkhKooeP70OdPxmOXZGcV+zxdffEGSxSwWc/I8IQ7G4RRVUaK1MAZdvH4leYsoovF8iVEc95yIo9GIxWKBck52bCnYgm8SM1EsTT9+ov/4z35M1Vlq5SBLUK6l2G+JnWOW5WQmZue1KUDUl5LEEGcpeZpijHhPVmStidOUstr298NEhkmSMZlMhPbN8ynILXAefUkfCtxsL755X4dzKuAKAPCEswrBIhx3advjEIC+TJgkCXuvHZEkSY+LqOva51/kb7Yeau6cYzQa9b0Pje+FAYFIp2l6wxMJrw3LjkF+Pjx/o/qgNcoeUYFv81rfNb6OReFfZxA6KK8h6X/9l4Hff5+D6OhdBu+fuLXLv83g5fxI0vHWLv8ug4ejOyZuvxzP9tgGeuDU8L067Co+GxV6JI5XrG4sPAKlbdhXO6q6xJiENBFNQqONF7GV1T9NMzLfSrvb7rh4/ZKyKjGxiKJMJhPyPOuTXA8fPKA4HLh4+YrxZMz9+/fZlQcuLi/Jm5zJvTPms0kfwx92BWkisOOqbnn69Cmz+ZzOdrIYFQeyUd6T4VrfXzCZTEX6LRIwjELyCyYyVE1D7SflZr8jyjMmsxFVJ3F8Xey5c3rCdx5+wPbqmq5uGXuEZWSkAzDLUpIoPiZ1A0gMjysglO9kQc58a/LeHfrNo+s66S58Swnudu+D3JuQGNRoRU+hF86vtfFivr6PpTsa4TD52bbtDUMOLnsoTe73e/b7fZ94rKoK51xPnBKOOURdDsFWw/JpWZa9eHCapn0SFI5hCYArK6ztboQV7zN+Vi3JEfDPA//+4On/XCn1q4h5fXbrtXcdiGMG8bbBy3MEdJY3trcZPPCG0d82+BvIyP7QN59T4Zo4eifD7rS2bWitw3iDwIcSSmnaVrLJ8eDmJGmKMYooMtR1PJBQk+5L21rKquYY0nSiKO06DkXNer0myTLaUmLSs7NTnLN9ou3O2RnWqwbNZ9LV516/4ln1jNa2zIsxJ/MZ6yt5/3a9pq5rstEYpaAqKvZmR9s21I2cLxuNSL2qUNXUdLbFKkucpRRVSRZFuKZFRymxibjabPjjH/8YgPFsRjIasTvsieKIcr+nLSvO7j4kjWOa7V4ahXz9PtYG4WeVuF9CL9UvBgHcc4yRvUKV9Z6LJ7YJC4J1VgRf7TFeHwKXwq59G8wUziv3QP51rbBkG238+q/7sKSqqhsJv3CeEA6EcCFN0zf4DEJSMCQnwwIyzA3YgUfSywT4ECFgJMbjMcvlkjiO3zhHFBm6Vt7/3qE5P7ts3AFY3nru3/hpj3fb4AG+yq2/ucPfMvCvMPhwfAigJO1dSN3DXIfnBimRpWk2OKbzrhs42/Qeg7VdXwZq2mNjjzFGhEa1SLUrLcQinT6SZaDk8S45oFTtY0npuBtPcpqmZrPZ0FZiJFdXV1xfXYuK9XIpqLuQA+ksm/WGyXjUS9133rsRIlnp3GybljSJqbuWVxevmMwnfauycxajRcTFOst6vWF0/y6260RvQ0e8enXJxaXkkyenC6Esqy0tDYftjpPJlCxO6JqWLEmlGjJUd/KZcmvFOzCR7q9RKUUUR72YTYiPOytxc9jJm7rpY3eXDsLNHo90M2sf3hvieOdcDwU3vrzaNo1UmjyATGmNjqTfY0grH0YAMRljKMuyzz0I2WrSl51DCXNYWgwLypDzYTjXw3yNvdjO5eUlZVkym81u5FLCMEo6XKNIvyV59u7xjUA0Dss4Ydx2m94w8Lft+hyrFeG4MDB4LSt9MPjwnv4fx5p2fw3OhxKDrkrjM9CSdGx9xjkiH48HLDhHI6+qShYaJ+y9Xdv5tlZx+0TfUPXZfu3r4HmWEGnN6vqas/MzludLqupA2XY8e/6U+USYkXabLU+++ILV5SVZnvPp974vbrA/f1GUvHz+gr0XQ3XOMhqNccBsNEXpHVVVM8pyDmXF6vqaH//ox/ziL/0i4HUak4Tr6xWHQrr9kjhhV26J65ZWNVyt1zj/vbedpa0bIh1RVgVtVfHo0+8zHU+gltbzJIl7nIVBsy/2tF1HkohQSyCq6bqOoizAOZHS4+bOHxlRoBqiCwMF3MTH+8N5Egwn7OLBAwyGGO690WJIndZ9qVJpkRvQvpIxZIQORh2OHXIFdV3f8ADCCDiHoYc7nLMBmj3czLRWdJ3zC43wM0rTU4P2eh1Du5H7//6LQRjfiEXBOdcnWoYGH147fmHwboMfGPmtXf5tK+4xfBjkD26N214FhKSV8u5lqA3LbpImqSjEIK5b38wSxXIw50TMpet6sFXvojqNa0NSCFEyynKy/MB+v2ez3TE/kb7+pq3ZbrbMc+kd6NqGpq1IRxnKaHaHHW3XkMRCEHs47Dkctr1wSe6be7Is4+PHH/H8xUtev74QD6cTvHxVFDx/InwNi8WcxXIp4q/bLbMH9yX8sZYOxXq95nAoSDLfRoyia1sa27Jbb7m7POPDh48YpSm7yytJtsZJTyWulKYsDuwPB8aTaY/FCB5XUQik+ghSskfGIRvc6Zr94SBS7X5haX11w0TmhqcZDDjgASaTifQIDOQHAZ9QVmCEDzGEKc65G+FkGCHHEBaeNE2FL3OQDAx5oNFo1CcKA0bB+AYzpYJX2hKYZJXyZW6tMEaRJDHn5+eCQfElV0lCHq8nhKlBQOl9xzdmUWjbhtsGDzeNfmjk8vzQ4G+WGIcT4J0GLyfrzxkWndsuoVIKL6tIoo/qxiHZEyZrFN3sjLSDNtywUGVp1l+fhEpRL1V2TI7JpE2S1MejNcX+wHa9xeiYzWEjCMBSEofWdpwtz4jiiCSNefHqOZeXlxilRO1aKeIo6RcFozW7/Y6iKPjgww+P4Jauo64qbNuBifjJ558DsFkvmM7nMuHbDizUdYN1CqsULy9eUzYN2XQGCHrRdpaqKGjKik9/7TtkcQKdZZTlZFFMnqY9HVvT1NA5in1B01miKBa9y67DWkdZlOx2OzKf45hOp8KGbeJeEjB8LoXyUnDHVuG27W4kHsOiEPI6SZKI2z5IKIv4kEHpYJThrqoBw9TNcCQk+cJmIcrUsvAME4/h/cGrkCoHuAE2xDnn0a+hxChdmcdci2E2m/pwJLBIOQJg61gSbUjiuFfbfp/xre7Dt+Pb8e24Mb4RnoJAT+OjF6AVYS+XNmMYViWcOyIJ3+YFwE3XfxhCvA2G/eeFZg8BMFpLVcE52xNaCK796Cm0bdtzQBgdoYzv2ERaqp0K3Aw+vrQOp3w9fjphuxX58dVqTds11HVD51oaz55stGE0yumc41AICchuL6rNtuswRtM1dd+jYZTGdR1V0/LyxSuKohAIchQxn844FCXaaA6+Q+/66orryyvoHOM8RyGuaWsdm92ei8trLEf26qIupRrQNJydnHJ+ekasDbZphR5dC5VdwGVUTUM+ykl3CbvdHnd6KrgMH1MH1z5UW/I870VilYcKj/K8Lw+naSqyfaGSwDGZF2DEIftf1/WNBGAYAbIe2txdyGFxrP2/LaeQeCh48FCDxxI+x23Q0TDR6HzVRKlQnbD9HAqVkcCCFXonQgjSdbbHwgDS71LVkivxnsX7jm/QonB0veSL8KWYt9j87dzCTQDKW1z/r3H0pSx/3rbrMJEWGTBP/e6cxVrdI/a6tsP4UpbDYX2ySGnAKZRydG3bu9PWWpzSWCuSZkoVBKivSKrLZAyIvnBNXVVR+clVeS3GoixJowhnW9C+ZOVARzE4y/XVFVESU5QlWZZJ2/RmR1WVvWozSvPyxQvyUc54NBJD8eHTfrPBaU0+SiiCZJnRVGXBOMv45R98n/loRGpidJKBn9TKWbzUByglzFCTEUVVs9vtiaKYyWRMkkRMZ9PeUOTzCkpP+evQngbPeBamAC4bbhihTh8qBk0j+pfr9RpjDKenp+Cclyb0FSgln8X5KkeA4WuvwTlMBIa8QBzHfcJxOK/hZg9CyCUMq1s65Mb84ia8DAFhqfrrb7uW0SjpjxPGcK53vjRLmI9/0UhWQgwGQ4NX/e/v2uW/boOHQa72xrFvgZ3E9OWxEqJQa8IX7xAgve2BLuEjaWN8hcP14Ke+rm2O4CcB0WhEI0Lq9kGyvLOdTB5z7OirvRBp2O07ry9QFQV1U1MrQ5qYPv6ty5rJZIaJIw77PfPkRMRUjCFLM/am8A05nh/BE5bUVcVoPOkZg9ZFydbvRi0NqRenORQHDts1P3j8l3j86BGxAm1dT8YqyTxHlAQK+Rbn+wImkzG73ZY0TYWZSUkexmjdfz9JcswXhJ4EkxjiSHZnIVA9cg6YHtim+mRgiOmLomC/3w+4Lvxxne9eNdoT1wadiBDv3xzDHMFQfGa4CLxr/vaVlFjuneRAZGGz6iZNvbVW7rOJPFlOACbJQhnO239Hzvb38X3HN2JRULyJ5f75GfyNV/rHt2/77cSkuI4Cp5V68xGB10+A4FoiIUHXNr4UafosttZH1y58BdZaHMLK1DSCnuy6jq5p6bDSUWigbIRWfLfdMs5HXjNB0TWtJ4OVBFblWtqq68OvznrvJY6ZzhY0TY02hu1+Rz7KibZbhlBw59c5FUU4rTiUBZvNhkPbUVuH1YrIJBSevrypKx4+uM9HHz4iUeC6DqelWcmpUA7WPT2dVBgOgIi4lHWNQ+jKQlOTGHJI0kl4hhKFcmOOUm4CFxcJwmN28GYlK7jyIroy68uHaoCh69PFWkmp1R2/i54c9VaZMTw3nMtD7MEwMTm8nvA++XuBaWursUrYk8J31LZyjXGcsFqtyPOM5XJJ11nvqdw+tvVqUtERvv0e4xuxKNysIvx048ateecu/3aDh4HR+19u/D54u1Hav+jA3sxYC7hFFgDl3LHd2kkvhG1FyiuOFWD6suZxFzl6R1prIh3RNAVRHDHKcyFHtdI0YyJFXcqi0LYdVV1TlgWK0DLrUaD+mqM4uuGNVUVJvd0TxynLO+eMx2PSLhOgVdehjCbyFO+q84u2QnbW4iBhk9Fo5HMqBevVtf/6HL/61/8ad5ZLoq7x1QHhWZTciXSY9sIrcYSpxCXXRjNfzMnznM5TtYf72fX8CwpUBFhvcB3SmiXgI+O0fz1MB/n7viHMz7eANjy69eoNI1dIVcP6BiNRGweUvmHUcp/fnt9623uGi0FYMKRKoUh03FcShtD5sHAprVgs5oASyoEeM2P7hUHKua4vo/7cEI0/z/HVbv3gi3/XLu9/eePrcbd/uv6xlD+P5zX+sQ4NOEo0HASMKYkpSxdQUIDwPVrrdxZ7zJmEstixdBkmjBX2Xg96MZ50I0pS6rYmiRPQDutkUahraZhynSX1EmkacT81CgNY15IEsFAS0znpoVNO1AdH4xEmEoKSpmloupbc+PKpUjRdS12Ke7zbe4qwKEEnCUrBbr+j9qSkH330IaenC2zXkcUJyolYSWslxBr2BoCHCEcRVgmNWD7KybLchwKykKVev/F4GxUo18fS85ONAAAgAElEQVTggkLFe19HXEIY4Xy3zz1c0IGeC6OfGu7YNNcvCBzLkcNz3PYSwvwZnmfoKYRwJjxnLb2SmCSqj2VOWTw8sMqHTKiQT6LHKRwXwKPwkO4GB36P8Y1ZFBx8aRz/lW794Im37vLO/+e4ceih0R9xEOFmHlfYcGkhTNBo0TbUQt5pLRJv+vMrv0gAHiIrO0LnWY0DMi24eZI8k3MIh6Ola1ts17HdbZlOZ5yOJ8SpqEc1bY2eyvGvL6+wpSNJYoTOOyHShjSOiZWhaxsR8PW7SNNIPVwrRd00NE1LNh4znkyI4pgsz9gXByKfka+Lg6g1GxFUrepaejtsR1vXdAr2+z1nS1Gp/uUf/pA0ijDWUewPxFGMidL/j713ibUsS/O7fuuxX+ec+4xnRmZWVlZ1N+3Gshoh4ZmF5AlCgCeAxMgYJpZgjlsgITEyYoTEFGQsIRCSJWDAAAuJl0QPaHtgN8Yut11ZlY+IyIi4z3P2az0YfGvtvc+5NyKjusrtKCl3d1bce+557LP3+r71Pf7f/5/SrTS/oAxxilwUdVXx4PyBOIEYJ77Hvu8lJQuRaOJ0PcWlmQR5lnw6xrg3XnwfaG265ws0YQYbqdRpWLwo3e/5ffLrnJ/xAMvH8/sddryWk5qH5zSfWyTGhJdJ9YsQ5ihHhqfkc/phTI4p1SASCZExOd1KCOEoMZR5SxRz3/HBOAVU/MXC+jtBQpx/vmeXv8/g5S33jZ7FjSQXlJYLavkacgFJQ0wUWVMIqmbgsppn5q0pEvRVT2pTSmm8nxeztUbyRN/TDzJNN4wy+JO+kLQZ08BPUZaEeLN3jlVVUtlCNBzKEjd0EzWXz0PRMTEGdZ1ED6mYWFYVVVNTJqLXi+tLcTbGcHu7Zbvb8ejxI5rVmi+++hqlNRXw2ZOnAPzg4WMqDDE6TNlQ1jUqtV69j0hdbCa2VajJmRk7szjHKKG7J+yhDaOOEEjF2bxbZ2frF/c5D0SpvWL2EnB0GFHkWzape8VA9DmlM9P75NSvz3JxacZh6QgOo4nDKOFuTWKegchrMf+Y6yhLWLYPgWEQYpYY4zSlmb+bbDrynX4tFaLeavDT7/u7vFazR933yvNiuM/gVX5gUQScY0KSI1i6pzh9dDrN6f3zacnOvsgTtfALDn1q0SmfaNEKbrbbRHqikpKRvOuSjXccpcBkrEiTr9fD9L7GGm5ubvj402dcphw+swXHKM/J6tNd14EP1GWFd35CToLCKEVhRBh3GAbi7a3oImpFUTUTyg8kPTHWcvXqmovLC+omzfsDf+5f+LN8++23XF1d8ds/+AwAk4a/FKATylMrg7Vy30K8uwFE4tQ2W8LIldaS/viAV5lHwEyV9twViHGJY8kF4HzbZ6zAsgWYK/e516+UmgRcXaKdz1gKjU6itjF1Y2bMA8yj1DkKyceSe3HZwoS5UPm2WgQs6yCaGKW4mlPOLDYzjn6qJSw/OzvHECZBtPc6PhCnEPcACXrhIfd3+SUzzlt2+Pzz4S6fDD7c53HiXT8kL737qITdcSpUhRDRMesKkGoG8ilFmupzo+ALrLWsVg3b7ZboRiFH6XucG1mKhcpCNWhTZsEfbm5uuLh4w2c//CHOOZ4+fTqBl6q6nvJtpWWx9V0HIeKGIek3iHMAoWwfvBPmpJA0AoaBfnT46Cnrns9/9OMEPRen8Pknn/AHf/C3IEaa1Upowxg5Ozvl8vKS4+PjCQCUd8hJfSkEEsWFOMwYUvH1MBVUk2Hmqns+sggrJF0Nk2sH7DkF58ZJrq1OsxhFUU75+9IZHN7rQwMdR9GHnKYofZw6TMS45wCW5CeH5ChzzWCODJYtxqktHSOKsJdGz3TumbFHJWpARYy5VhXTd/ZU1ULGLuQhsF/AI/CBOAWFjNEeGjy8fZe/z+CBA6P/xQx+74k5CuEg3VCLj5UsYW+BhSg7lzULFe0ovf6+76mbhqIo6Hu5iSFExmGkWdWYjGjMuWwihx2Ggb5zXF5c8oPPIo+fPGboh2kXKcuSsipTJKBBRVzwlMZikhDuUiEKK+O/JgZ0jPjgKKwIzigFzUpGf1++FBKtk5OTCeDj3IgbRlqlWDUNf+fv/l0uLy75nd/5nel8hmGYhE5gLrBKNLj4jrlOlEaTVWK7Tsrz+7tq6t7k9y8Ki9MDZZU/JyanodIu6va6Lcvo4G24lyX+QCvN4AeGvken7s3+UpG0MTvCzMGYneGyXrD82S/qKPmc9ja9lEJMyMl8DX0axEvI2MKWDIM4BzH6masR5jX5i4CW8vH97MP3x/fH98fe8WFECkrN/eh7inv3h/5vjwLk5Qd/uScKmH9M5cPp8bl+Mfc8crEoTgW/kJ4goJp5FxB9STN9B2sNPijcMHJ7e0tVVxRFSde2IkdWlShlpvDYe884jNiqRqtUYLJCQda2LR89+4jbm5upZlGWJevNhuurKxG1DTJK7LVHo/BhEGmxtGtUVrgM2n7AWENZFBS2mBiF1nXD5cUFr1+/keul4csvv0yF0Tmvvby85EX7gqdPnnKSJOBgrsgvi20gKE50IkdZdFvmWyW7Y04L8rUYnGdJ7CsUY5ZxdJRVriXIjisp22ISldz603th9LJbsDzXKYpIu7LgAGBtNovzTd0QNbM3LVOTJWPSYddjWiNKTa3T3DoexpGiMHO9Rc1alUzfBYIL+JjrVVpYvUzJUppQCqOpNqPnuZT3OT4IpwD7ldmltb9X6H/4pNxhmMLV+w1+fqkYfbhTjYn3/ISA26b2VcLh566DyjdqQZBhNCZV0ock/mFtkYRKw2LENt1obZJ+hCdoqOqS0W25un7N199UmFJTVw1dJzWFpml48vQJu90OozVFWdJHUAFUBGWkdWfs3DIzSlFYQ1lJqqBiTDUHIRu93bVTTWS72/Hm4oJIpCwrRj/igsxNfPrxp/zu7/4uq9VqogPLhDmH4XqIATwTXX5YLvSQn5/vr5oq/mUlBppDYWtsmh1RqZU7v49J3Af7d22mQt9bJmpOZe4Dz8keJWmftcWkap3RrDlkl3ump++dH9sjV4niCIyecNSCPg2zA7B25oKU3tXSoSRGJp0l6H2SsCvSEFaGOufvlJwSkbbd4cZfcfdBKfVfAf8K8DLG+KfTY+eIbNwPES7GfzPGeKHkrP5z4F8GdsC/HWP8W+96/8yMI1/mu3P9pcGr3LY43HYm25f/fZfB3/2NXLE8fACUUFzN3YKYsXpyXtPHLxxK+tFYy7qsGMZe2JrKEu0dUYEtC2xpp+fXxhBiqjfUNX3f0bYDNzdXvHz+gvVqMy3+p08fc3JyzKtvX7HbbvEu8QSYYlpEo3fsbgRcNAwDZ+cPqFcrClvgXQBGtLEorRn6nrZtefHypTzfjfjgE5/gFh899WrFw0eP+I3f+DEff/wxu91u2o0y7Xm+l/PuyB0ik/n6zw5hXtjJ0JVOqLF0HY3oRy6hu2pxv5SazwGYh4oOooHDNZeLc/mcldJYKy3k7W7HqqmxtiQEL2zcYWZgPqRWy8NOc5tacCdzt3FJjJKQiqkekrlDDs/HJwIcNW1uEWN0mm0I8+OkDlZMorNunqh9n+N9I4W/BvwXwF9fPPZXgP81xvhXlVJ/Jf3+HyCU77+Z/vuziA7En33nu0fmQlQKT6f64tLo7zH4qRp835ve+Wl6U/YNPv1+d7N4y3MXn5LePAQ/Eb/I7/s96Xy+Pjq0FcUmYkQZgdAuVZEF7mvQ3hONoakrfDgi+htur65pqjWFKdAZ4ac0x8cnfPTRR3z55Ze8ev5CyGQ3R4loJaIiE+DGaCtiqFXN6DyxH8QAzQBRoYxUz58/fw7A6dkZP/z8c/q+w9qCJ0+fcHIualSPzh9MA0VLcdXcrpt3YLm/Gb67qKFJ+rUI9TNT0CRLh6yDbNwmTzAmTs1FNXq62wJdnrid0ufN3Z1suPk+ZSOcowmN1jZ1SRTeBbyLKDXPkNzBOKR7Z4yZyFVUSgEkZZoRj9rMLE4xBtyYmZdIKXScOlkgRDT53CbngyBry7LA+4BSc4s0T9OO48DFxSu++erLt67hw+O9nEKM8f9QSv3w4OG/APyL6ef/GvjfEKfwF4C/HuVq/b5S6lTt077fOZRSk/rPbPeLwP49w3p5szs/8FaDn552zx+n2sbdT1GyutFpZasQseipx61Nmom474wji8WQHWGqZufKPHGC2xqthRfBeW7VLX3bCWEqUfqJyEBUU1c8/eipQIQLGfBx/ZB2Ck9ZFJw1MgrdNA26KIQKXYkK02q1EXTmMFKUNTGM/Jk/82cAODs/5fMf/5jXr19zdXUtr08t2Qzg8d5PakZLZN8SRJMnJGdDWlx3pRa7qFwUmUhc4FEWz/fe44NQ4KmUO+f3LEupz0xTpzq3/sxksMt6w3Jnn6IaBNacKdS9d3t1qMjswOYls5+GZBRjCGHCiCxh0MGHNDkqDiNEGX6Tcw3pO+RJ2GGWk1ciTgMRH8Twc8s1D38Zo/AOYggMfQvhTwa89CQbeozxG6XU4/T4x8DPF8/7Mj32VqcQ05fLv+3/bXHcZ/D35IL7z3+HwU8fsMjdFv/mJXDnHSJC5pk+f+//UhioyYwQc2ibW14hhr13XeL28/sL54DkoWVhoREugxAi0Qf6tqNZiREO3cDV9Q3nZ+d8+slnrJsN4zBy8fo1/TBwe33DKnEhgHD/9cNACJGyaaibhrKs2bUt2lqKouBofcxHtez8dV1TFgWr1Zrb21vG0aETzDa3wZaGtszd95iJD3EJiwub+Q/2HMqCXyD4MEGfJVLQFGVBYUucz+1HMVfx1XOxMofh81j07BAyrV4mWJ3Qh+ncC2sprGV0i5mGHLzqffn3/H3ze06PkXQs9dzazC3nXBtxYyqYJo2QHGlkpzCOKaqJIjUo7VuZBrVWxsq9d9PQWD5Pay2bzQY3tLzv8U+i0Hifld7d0BdaknVZEFVkaYqLJ77jU97yt7fs8ncNXn5bvst+ODhXJQ4/PKcEy6pwPrTSeESyS/6W0wI1FcV0IumMMZDHlJfDOFKckiKmNYaiKeBcUZU1u13H1eUVw5Bk1/oBjKbr+sQCXeO8p1mvwRiub27oxpGYFIyLwtBsNlL1TgCjXbtj13acnp1L4dN7NikdEPWjXQJYabwbcW5Od7LRL2HEh8KnxhhZ74vrO+M95qiQFOYbY1BIehCi9J1yyz1qGSuPUbgm0qXac76HnYVDzcclgjQ7haIoZm7HuJhNIXWc0j3K9Y9DJ5e/S2Z1yp2I7HxCQkkC2CLNnqRZDam1QAweped0IUc7IUUSw+CxIRJMSCQsduq+hDh3dMQ5SfHaeYd3451zfdvxyziFFzktUEp9BLxMj38JfLp43ifA14cvjgstyePNaqa1gXcYfJzqCW8z+Pzzfbv8XYOXfw+TA8iGrN56KnOVPJOizFXk+Suo/RcsUiFtkxZjkELl4UeEEIRvwMdplt4Yw/HJEW0nxCYvXgi4SBvDTbejKAvOTk8piwqlpA3V9T1Hp6esmxUhUR1JZFAmzgXo+4GbdkeRYNXGGIaxnxZsUYi+Y13XQvuWiUSMkZrIYuHnc8+sxvlxay1FkR0habdNOP209Sr2pdzzfTDaoOOcX0cnUcGyDjPfs/n6HUJ+YY5ils+NMU6K1DN9mqR5wYdJcUorlXGFqcR1V/05H5m5OfM2oBR920+cEKv1eoadK4VK4/Wj9xgzp0PL7kbwIUUEifAFkzbTzMMwa41474kBul7u4yFC9F3HL+MU/ifgLwJ/Nf37Py4e//eVUv8dUmC8elc9YTpyQRXeksfP/941+HiP075vl983+OmRw+Dk3j/kCnX6Lc4FM6kLpHmLxd/DclckD8fIb2NqTUrxcX+hHi6wJTRXK8vJyTH9OHJ5fQ3A9vqai+tryromfiYchbOeoYiUVqtmikSKyhIiMmmHCLwqbTg9P8fWtQjXKEWftB6VUjLUpBTr9UZmJdJ3dqPDlU7UmlJLchzHyWBzm05CajVdJymupuu9wAwcwtmne6LUXj6+hBIfOpHD1uPyOsKCUi+d66TWtXAK3vusmHnn0EpPbFSHCMIMf8/nt5STk8/2+WSmTWNpsDEKlVqONpYyc9576rqSVnHqwMUYpv+CT8pi6T2Vlr+jZsj9+xzv25L8b5Gi4kOl1JfAf4w4g/9eKfXvAj8D/o309P8ZaUf+Q6Ql+Zfe60ziu/L4Q6N/u8Gn85X3uOee3m/w+58RQabwlp8whbjz86dKupI9TynwShGiKEXFxQkI4WacKuLDOKDSbhcTp39+72koxzlpx2mpMhtjCSiqpsYU5cShWJSVZJdRFvzVtegwyDDVisvrK7bbLUWqEaxWNVVZUZQl69WGsq7ohgFlNM1qhXcOPRji4jw2SfPw9PSU7XbLMA4EH+i6HqNkQjBrGsjlinMaoHKLbb6Ch4YM7EUJ97Wml89bOoXlZy6Pw1ThcCdfOt/sTPJ3yNwJ1ogwTfAedB7ZVlMBd7mMlq3NPC/RdR06FYvdQtZtCSaaO1ipQ1HoiVQ2O4VhGEQizxjhvQgyLKZ1IpYlpzXp+yAzLl9//RV9u6Ogv3M933a8b/fh33rLn/78Pc+NwL/33meQDjXdr7fn8fBug5+edc8u/06DlxN/a4nzvjw4n4sxluBjWmQqFR4hKjWFdkopUf2dCFZSSJT/jkqVZ1nkzjt5LH0NYyy2TMIho5MCUlnw5CMZVVbasN4cc3NzQ1mWvL54w/MXL3hz8QZbFKySUtI3ryTdODrasNlsePzkCbYRx+AUBK14+PQR4zByeXVJlxSXjDb0/YgbPUUhBcCmWrHdbRn7AWeLPVwALMhkF9OD+VoeRgLLx+5Thz5MBXL4vIwc7nMiy8Jndgq5TpDPyxgzFRmNMft6jKluMPElZko1HRYOLu/2M91ajHGSilsqUdV1PTl8790cXSqmmYYQAuOYnJnRuFach7B5X3JVXnJyIsxUtigwxqbMW9ZVLjSO40DftYzdjuidcH+85/H97MP3x/fH98fe8cHAnOGXjwLkN+5EAnd3kYOYYAr7Dp9/d/dZvkbC3CxKm2m6587E1E0wOqkWL0LY3C5jLhTZBEPGIVoQKYJQCGWaC4FhHKnqBlPIDg+wXm8wtmS1XtE0DWcPzjl78IC23dG2Lbe7HeM48vmPfiSfqST/33Y7uHgjdHJacXV7xTcvnnN2doaxhiLt/u12hw+ezXoj/AZKUZSSLnRdR1lWe3n6stW3VFM+pGDLR6YxW0YQOZVYqi0fRhfLn5d1l+X75r/n88lpw/Jzi6KYfp4UmZybOgw2FtICTMVlxZKabY5GlpFCritkDsgMUbZ6bkmGGOa1nlrbWmth1TKaQhUT7HkYBl69+pamWdGsVlPx+cHDhxS6xIWR0YkeCEA37Lh48wIVR0oVUnH2/Y4PwikowKTQ++7x7tD/Xlj0PX//RQz+3uOgphBCajmmgpFRGpdkv5SSECwuOipT1yExGZMr13EJwc5GJZ+Tc17vPT6CMQUPHj6gWq3ZbI6pkgSdD5EQESq1VcPp+TmboyN2XYsympcvX/KTn/xESFSAsi5R3tN1Qq222+6wRQkh8vzFcyKK4/WGqhCn0Cdh2rquJcSNEWssZVGIXmEqiC1pzTOoaVm8W/5taeDZEDOEd2nkuV2YQ//8PLkHs9Hn/5b8BIecBhmktNQYyYZ7WNgcRmHDzjRokHAPizRorw60cF75u+d5COkcHBilmrc2QfOqaSBN6Tp1f/oppVyvV2zWK9pdy5tvv+Xx40cMXUvfDTL27kZc8NPnODcKbT/qF17qH4RTgGzwd2sJ79rlDw1+fuwXvArpTX6RV2Uwjk81Am0WxJ/KgFZyQ4Co0gJWadiJOefO574c2Mn8egpAG3ZtR1kazh+ccX7+AJSZ5hRASDp1iLjoMFaIT7txYGUUo/M8fPSIbuj55htpAvWDtMrQGj8KS1PcdYyjo+t6trctv/Pbv816JYQdWdexqmvatp1IY8qqYrVeobWe8vLp2sBU0V+nmsayiHiIHTgsBC6dwu2t6F6enwsHZKY9W84aHBr14WO5cJePHJlkZ7NkX4IkujKKFoSva2yQnT8eFBYz6G4ZieT7WNd1IuIR4hStzV4bMs+CoCQ69t4RyFOimhCliCzLQPPk8RNevHjBq1ffUhaWzdER3nm6vsPniCJR7vV9z9D3COM1MrDznscH4RQizAW4/Ng9xb0/KYN/n0NSBj13IIhpai+gYsSgiXlgJ32B7PV98GnHFeBSdihulAWzXq1RSnFzeY22Bo2lKmrqssG7iC3kc/OMkLGa0TsymYkxhvVqRdtr3HbLerXm889/xO2NsDC/eiNhqAYIkWHwtLuWmCi+bq63nByf8MmzjwCJQLKCM8lQ3eiwheX87FwIZIZhD6eglGK1Wk07ZW7BZpq3ZaFwySu4TAOWaMm+77m5ER7K1Wq1V+m/jxT1ME0Q9e3dHcq07Iyyg8hRTVEUorIVQgJIzVoeMrEpCt15u/fe44Yxne+YrldkvV4LIEkpMfppLUhS6L2fIP55WMylfsKSrEWrggcPHtA0NW/eHNPU0nZW5EhL2nfdbgtA17ZyDUJ2wL9msnF5oX0oBn/3M+6emVIZKafyCAJGS98+pvA6pww5d0xxg+DvoyPTc2tliGHOh8ehh6CoypJdO7A6Wk88jD4ECF7anXlAKNF9iyR7zzDMoXzOy8/OTnn27GNA6NhLW2KsIZrI9fUNt9c3VGVN8JGbm1tePn/Jw1PZmctK5gV0MibnHcM4yuxBXQkpqFJT5T7n53PoPbcRD1OEfOTdGvYBRlprqdov6MuXw1f3tTbz6/LveRfv+/5Ozr+shYQQJqfQ1DV92+L9PDAlNzCdd+JrNMnR3263s3q4MbS7HeM4cHpykkhxHUvGbpDd35AAXSkVlWuSRqyL5eYYGNzAer2hLEuGYRAKNr9FuYKIZxx7ri9Eb7Pd3Yr2yOJ6vO/xYTgF4K0O4Z+S0X/nS6LwAShjZjILRRp/joxeRDrkkHpJVFJsMlozhIAp7KRopKKaIwYnTJvGWLSG0/Mz1usjcQxGCDxHN4epGaCCUrRtizGaZrXGoCmMZXAjVhs+evoEgOcvvkErzSrNPKzXR3z0FIKXHdA/Cjx59IhhkN5213vqqqIoionJeBgGyqKQ7xXCxCEBs3EuMQDLUH7Ja3jf3/PrchFwvV7v7ZrZyA9bmEsns8Qg5GjjxYsXjOPIw4cPZZ7jYMR7n0NRUxYFwQSs1fgouISp0BhlYMmncL3dbimsXI+yNDRVzeAc/dBPU4yR2YnIqlBTMXl5aD1rOsyTp4oifdfr61RMbFvavksF4UjX7giZNyFE/Dhi1K+pGIwUef8Jmf4fw+CBVAiacQdTnJi7H6nCThI7DXE2UqMVsNCG1HMKpHIFO84c/W7wFFpPkYWxhkIXjKPj7MEjHjx8QN2sKIoSFwKj8wQ1TEIpgxPsQtZxuLm5pShLqqpk9CPKjSjFlJP/xuc/YhhkQKepV/ypf+YB52fntG3Lrm0x2tDUDV9+LeO2252kHd576qahqkqc87RdR2GtpBfOoeOcBuQjRwjZgHOdYam3mJ+zjAam25dy/rqu93AHGYmYgULDMOy9dvlzWZasViuUUnRdx9XVFacnpxhtGIeBpmkYhwFblPiUwmmlKPa4FsNMiJI/fxynDtMwDFxfXaON5vGTJxwfH9MngBoqMz/NStt5WnQSeElzJVpL9OjiImWRM8J5LyLE6ZyEnr9PIHnPbrdF5ylJcg08R0HvbwUfhFP4Yx+/rMHD1ArKj6cS8yJCkcr+9DmprhFDzkOBKLJlPoYE/DCL58qEs0qhZwhCrKq1RjZ4hdEH0N4ojMZNXXN2fka9atDaoo1GKyax1mXl26RJy7IoaLuWizdvOD8/Q8mUDX3XcnQslGmf/uBTLi8uscpQViVlYQnBs16tWDWrNIKrOD46AuDi8g0Xb97QPXvGyekJdVXT0yfSkExVP3MfLOsCy918Ggx6y1zC4eNaa8qynMRfDxGJh6Ir+bMPEZHZcRwdHYlTuLhk9+AhJnFZrJrVLKabh6ScS6jBDKoyKESxKkdpwzgKhwYZ4NWL0zOGTz75hKPNBm0tXdtNtYysEzEMA3VVT8AoaWPnTpuokC+LzxM3g7GAOIY8uFbXBX3n8OOIzoWmICmsyrWPX7dI4b7jlwoc9nb5dxk87Bl9bjNlw4/L4uZhpyMv+nkRG21SHpdiiz28u7A7CywatEkkIjFNTAam/BUUN7dbnn30MU3TECO44PCDMP5k/v+c/4ZRCF50afAOhkHRdy1witaapqm5vb2dzvP4+JjHDx+mnSlfE08MqS0WPd5Hjo/FKWzWa776+isuLi44OT0Ryfd+hs3mHTunD7momEP+5Y53iD8A9lqEcr32xVjvU1zKz1sSpSyLjsuoI0cmjx8/RmvNz3/2c0Y30qVW65jqJX4xa+C8Z3Aj5cIZxSg8F+M44NyId45hQb3mvOPNmzfs+o7Hjx9TVOU0VDWRn6RIZBgGrLEUhZ2Kznnt5bkKocWfGpcTvDo7j/V6LVFS6PF+FCHfvC1NaFnQmJnJ+z2OD8YpvLcTeKfBp1+mwg0zleU9Bi8Pz0a/NPi983lbXSOGabhTK4WxNlGm74d+k/HlOkRIcxFaXkeKOqrUforAJ7/9KatmTVGW+CA4hSlPjloKmTn3LrSM5FqLN4KV2G5vpeCnNX4cefP61UTZfnx0zLNnz1K+mUZ5jcWNowi1xIh3M3/B6fExX3zxBc9fPOejZ8+oypKua2m7lhACNbLj2WIWXRlSN2IYhqmot6w1LLsD82WW0eAM8oqRqWC5dBLZyPP3zwNYmdY94xGWTifGSF3XHB8fc3J8jDWWrpfuQt91VFU1GVw+hl4k8UxdYZKoTYiRru8lX0dNkcJqteLk+CPH/XwAACAASURBVITtrRQcb25u2BwfYY0hKJllcN5N9QMZeBpTyzI5vZR2ymi6J+pli1QivnyEGDDWUlYF3cWOvu+l7pAcrI4eowzWJvDYfev3LccH4xSA+w0emEv37zZ4+fH+XX4y8vsM/J7H1MEvb5uYyzlMbjVJfURajcvC29xuC/PzQhrWIUDUU2vq5nbLZnNMWZQMXnL1mcEoElyqoKv9DoNCGhKFNSll2GGLgnbX8vLFc169fg0IVuLF8284Oz2jaRqc8xwfHUlP3qUKfFQMY1pgRvPw4UO2t1v+4U9+wqeffsqqaXgV4fb2FpOIWfICztLuIXiCVwmPb4WYNDi8l/sxU431c9icQt3c2XnbxCTsdwuWj+eawyElHgjr1MNHD9Ha8PrVK263W+qm5kSdTCzT+T127Y5CG9ZLnosQGMeRoetZVzVqcY8/+ugjqqri4uqSvusIzkFVTjRyMcSJtFZEgCRqyKSwMY1FZ9Xw5fnH1ApdFmHHYeB26Li9uUKpwKquKXKHpvOg5DX92NOP78/R+P3sw/fH98f3x97xQUQKSiEMQJG9KAByJBDkb98VBcCdXf++rsadPf+eSOBwdzp8kWQsEraPbpRcVEmJ0SQK8vvCY8igncjgkmakMqA0261MJRaFRRszISH9MKILK0OVEVRAagBTrVShvPAyBKRVlqf/rLX0fcdu13J9KfwLtrC8fPkCTWDoVlxeXvCPuo6mWVNWNd47mqqZpjC1rfj42TO++uor/v7f+39xbuDB2QMIok/RdSPCkpT5F0AhE3tD8NjBYGqF9wowCatwWENYthUjPhGKGGP3AE/5nuY0YRkdLBmfxnHkZWKjPjo64tGjR6koahnWG7bbW6FGizJ5mmsJh23PVVml1MRPub53jrZtaYp5MrRYCZjo8ZPHrDcbkb5LaUxObfe0JmKmsotTJCGcjHM9ZckLIUxUuY4i0O92t2O7vZ1Up4P3pAFL+q5NiEZRCxuWNG3fcXwQTiFGUnvt0OCBd4T+b2tjLu1Xqbsd2jsGv3jRoqm1BzS5+xoBokRkYMlqJT1rbTEmt5uWePe5YDQVrkJCNFotcxLpKcfHp4ze0Q/jNC8/tTIzBMqHySkYWwBKDClxAxW24Ob6huA8fdfx8MFD6aMDDx89oLRSULRGUxaWn//sBevVmpOTM66uLrFGU1fy/OOzhxytN6yqiqvXr/mDN695+vgJj548BW0ITgqgWdXaFhatVaJ991IrSYW8SdxEBszlu8T9ezkDjURnIjvYZcEv9+9dqtIv/5a7Ftmo2kRDN7VAlRDIFFbQlj7MPJJLDobN0RFxdLhxpLRW0jMj8OPr62uasqJJoDKtNFppyrrEWmGqskZIbpSS3UIpRZWUvM/PH6CUpA5aq0U3BaktOFnruWZRFIXM2gRxCFdX1/R9xzgMBBzgiP1IoWbmJWMN2mgaW2DLkj/84jXvc3wgTkGKK78Kg8+P3/eitxn83dekyu3e6+53UsTcZ5cq/jjMGoYmi76qDHaZmoj5LCDmhW04PTkD4PTklHGQ6KOqhR4tph1CobHaJL3FVLSKI0M7sBtabtottrRYa3n58lusNdSrho+fPZvAUVVZQXCCVbBQFQWltfhxwMRAYy3XV1d8/bOfydcMilWzorKG0hp22y03l5ccbY5ZH58IzLnrMUUqBGpFYQuc81xdXbJer6nrakEVtk/Sm6djl4VEqVGIpsGSN0Eue9xDP+YdeLm7Wmsn1aol0CnGiLZmwldMBcY4z6/kczDaUK7KqW6h0FijKEubCFCGheL33IY1RmZfxNidvFLLmsvfxRgzOSOtTeqipKhB7Q97QWKIGgeury558/o1u90WYqAqLeOgpDAZPDF1GTYnm0nhWym1992+6/ggnAKkqvzi96lyf/C87zZ4+W3PoN9h8PNr70lFwqIZGQ+cQopatBZ1KJ2IPSlsalPuQ0udy+jDKRuS7xLARU9ZWlYrGRyy1jI4hzaGMfXLK1OxWa8prKXfttxeX/PihYTHtzc3GG1ll1pXRBUQCTHP9fUtDx894Oz0ZIpc3OjodrfSHvPSStys17S7He1OkHlHR0dcXwlkVtvnVKsVfduxWa2wWlGXpShMFQXBOXrvKVOJSiGCwXVds9sJsGi73VE3YkDD6GSIaxE1TMVSlS/1DFVeTjnmNbBMHZYFx6w4ba3lwYMH6dq7vbDcGCOtXlKL0UlRUkezF+oba6jKaiG5J45nszni8x/9iKYsZ/q9ZZM7r2UlnN4ShQhrU3aM+V7kf2daNZPOeSTGMCEmc8py8eYVu9tbtBGnFT1U1lLqikHeUK6R1sLuldOr4ddsdBoQcZRfMKyfhGL2jnxDlq+73+Dnv5BW4jJ9Sf/eiVhi/n/5bQIxyfnLTVeJQSfn2IJmU+nEQk6PAmhd0FQNp2fnk5Hsdh2mkClIHwL9OAoIaRhor655/fwFr16+4ioNCO3aLgGdTnn64x8So2McPE1TSydETpSjtfAvdH1Pu5OWpYuBqiw5PTnBWkt7uyXEyMnR0QS0ub254eL1G4xSNE1NCJ66KSm0ojCKwTsiEHzCG1jJwTebNVqriafAjcIoZa0lJrEXyLu9Sq05PXEFSDttMRS0QEDCPIadlZMOcQ15OnM59ASpO5JQjE5LmC/8l2riMrQpZSmUwNJzkDd6gR6fnp4QfUDlP6Tz2JvhSRwKEgdJ52Em+DUYren7jhjdFMk4N+BGR99u0dGz28qAU991dF3L2LZoAgaDjgHXjxIdhBHvZs5IH3YyFZq7YO8PU/hup6Dul4z7z4B/FRiAPwL+UozxUin1Q+DvAX8/vfz3Y4x/+bs/I+VRhwYvPyyOQ4Ofb8idZOPOLr8w+Ljw7PtVyrfiJWZ/JY5Ip/ZpiLlIlnLHqKfPjNOcbVgUmRRkyGziXizKmrKsJ74Dn/rwGA2DQwE3l2/or274+h/8Q/rbrTAMlwljED3Pnn7Cx88+Rm1WjEpwBpvNmrqq0Ci6tt/DJNR1wzD0uEHg0uvNhma14rasaLuOatXw6JFIeey6gdvrS4a+o7AaHT3b2yuauuLk/AwdI303TJRfTRq5jjFzJAiYqOs7oZ3TChUE0gs5fZojA6UU2+0txhhWq8302CGMORv6oXNYFguXRzbaEILk9jHivKAli7KclJzkAyJlUSTuTT3R0ytySK5Bx0kbMgap8fgYsMbgYxTqNQ9KRQhaNoPURh6cm4w4p4GFtQz9QN+2uKGj290ypHpIjnasEfJeqbMYnBsxBjweW9rFNGSkrOupdPOLTBC9T6Tw17grGfc3gd+LMTql1H8K/B6iDgXwRzHG333vM0iH1up+g4e7Rv8Wg5df416nYvEmbzV4YNrFJWCZz2We6FvkeDGTtkbKqpSRaJUZl1TKDfTs4JL4Zw6TlbIYq8FYrC1R2nB7s0WpdDsqC9oQEcUgayJffPFTXnzxJasBjq3BNiVjil17a/nhs495+uwZF2FgTJfFh8AwChdg13dTX1xrw8nJCaum5urqkm9fveJoc8THH39C83nF5dUV7a5lvU6h6MVlmqm4lvTBaPqu5/b2Bms0tirxYU7/ZO3PBiy5tUyRjoMAjUolrFXydwCNtTNC1NpiMuCMgViiIjOaMefwS/am5SRmfv7hkQlQtU7TnGm4y2fOBSUOO+FQiT7g05oa+l5SRqVmct4kChyQuQVJC9I1iRCi1Bay8LVzI+PoIXqIgRg8Q9vSda1wKw49Q9cxDpkzUkh9hLXZEXxypMaglYFCgHP5yOCoDP7yv0rmpXiPZFyM8X9Z/Pr7wL/+3p/4liM7gbgI69OHkSv22c5/VQYvf5uN/tDgc3uURUFzBldpodAOiqDTAolzCrTMhASoE1MxTHagplnT+RG0xpYVxjMNOGkKdtuWuraEfmDsO8bbHWYIPF6tqaIIumxTm+los+L89JiqKinHND+QahVVWU4Am9vU8uyHgZVuOD49lUm+r76mKHpciNTrNY+qisvLK7ZpNr9uGtCGzeaIuioZ+nZqo61WDdX6iG3b0/YCG1bGUJQFWmmKokzpgJNRYWMSSCdMN0EnOK/WhqqSrsHx8TEhzEXEQ42HXDTMnYlccMyOYukUlpHGNOkZxOHmhTPPICyWnRJTDFHIdHwIaGUpCwFtlWWZpmHT6LfRUgcIMhSnomzTwtCVayO5rjMy9j0xiAqUHwZubq7x44BSkeAc0XsKmx2nWIjRmpBSpfx+jkhUPp1n5rSQtnRRJUXqP+HR6X8HUZ/Ox+dKqb8NXAP/UYzx//zOd4gxobjuM3j47rB+X4dQHRp8SMqOhwYPe0Z/aPBTuD8nMyxCCPlLLjBOPfRcPFITqYo2suBlsRqaZi272zhgSy3Tkosptos3F0QdefjgFN+20LaoduCBNZyiaXzAKCjSSz7+7DMen5ywi566LHEqyYx5wCRqMmOmsN4T6IYBWxYorXFBSFKGYWD0jma1Yu39dBeMMdxe3dK1W8qqpGlKxtFR1Q0ohS0s4zhOvX7rRm6ur0EJdDt3BcZhpFxnBOJyHkLuWQhCEZenIvMFf9sA1ZJBOncflmPZyw7WpPbkRR9BaYXSkuoJBNlTFHrPqXuXdyGmSHAZsSxZliJR+ClyVJuK0VNlWc1KVABj19N1LX4Y8F7qCNvbGxSRuixQ0cvE43SNZP1YY6e1qxAH770nGk2h9TTuHoJ0xHKkEQ9SqXcdv5RTUEr9h4AD/pv00DfAD2KMr5VS/zzwPyil/tkY4/U9r51k48rC3OWwm54He7v8nsGHiRo7e43J6POCeKfBy3tPRn9g8Kh9aThgccOlVmkQ7621hgjjBBI5xFQEnAscbdas1w2Xl9dCxOEcvnTUpqRr02z+7paoPOb0iLP1mn/8k3+Eur7lUVFwFAJrbbjabSdmp/OzM0xpcEHITlSMlGWB9obd0ONDoC5XUxGtKAv6YeDN5cDResOrN69ldLprabuO9XozcRiAdE7qZs3gBkpr5X5FiSCKshIa+c0RKoF5hPBDVI+7tqNtW1arFVVVMowFhWWak5BrE6doSuuQajG5OKtS8XFWbM73dzk/sYwW8iRm3ly8C1gFhVb04yDkusaiQsBYoXU/1JJcDlfleYVl+pIdUq5bSGomU4y2sAJe8jLHQQjT6PSQirdde8vQ9bhxwI0D3jsMkRi8KFCrtPjTIhrGIQGYZN1rpTFlgTVCCJxrFSYVdspSU1iZmI0hptTn/Y4/tlNQSv1FpAD552O6kjHGHkR1Isb4B0qpPwJ+C/h/Dl8fF7Jxm1UVZRed3nvO4RdG/10GDyyMftHukn84NHhgz+izwS9xCMvnTj/mpkeqGOcQNveZtcq8fPP3DSHStT2b9TEhwE9/+lN2fce2a3l8/pCjZkObpvZsU7LZrDk72lCHwNW3r6iVZm0tZoxEP2JCnFBq5w/PYdWghlaGnGLaNRWs7IqgJKS3qVddhooh7dzbXctnn31GXTdS2EzXqGoabBrQ8t5jy5LROUorVG/KaGxRUa7WBGV4+vGn0675+tU39L1IlVmrcG6cdkijDaEMac4jh/fzddJ6Rjt6LwNnWkViONDRSF2KQIqMFSgj0YacR5gMduh6qGshikncltl1W2sn/sblHEVWaFqOL+fI03u/kJeTc+o6QY1aa6mpEt0aKQWRDWEcB9qtTKv2fYsfR4J3gi/wntKK43DjgC6spBy5f+b91LXKbFF936bz0lK8jnPHyw2gGoVGxvAL8/6m/seafVBK/UtIYfFfizHuFo8/UkpclVLqR8BvAv/oj/MZ3x/fH98f/3SO92lJ3icZ93tABfzNFFLl1uOfA/4TpZRDYHt/Ocb45n1OJGRSy0UUANwT+qu90H8vCkivuy/0vy8KmF+6KA6q5ClVbpGm1GW+IHuPQxL3DClUTClEbo/JS7S0mvoBUp++aRr6YeT87AxrjHQH0s6/LtY8OD2l0oabV69h9DxY1ZQRqSUYBV7RmMQneLzhKjh0LXDiEs0wenSMuOgZnfTH80y9rUtqRNK870fOHz7CKMPJ6SlFVYMyRPQU4q/XJbbs2ByfoogUNusjaKI2MtZt7FTMquuGECJ979FGOh1vLi7Y7nZpKlRSmPlWZ9ahFB2kbk1ASUVdBfzYkYWTy6qiLAWJGHwkc1XkNZFvb97hb66vGfoec36OUXn8XCLOPNbd9z1VVc3rcdHdyEjFHKXmaGF5GGPoB0d0I7QiD2h0wlwMvURLfc/V5aV8h1ImWXUUCjyjhAxFA3WVU5P9+lpOiXIXKWNQpCgqUWptUwoXAq7tUMYkAp5FOPYdx/t0H+6TjPsv3/LcvwH8jff+9OmFEvabA4Of/vc7cv0pz+ftof99Bi8/5npF/qB9g88fcF9GllObcXTTmK+8XmOtwiSjjYlMZdWssEYYeh4/fsLx6SlRQ3uzw/UDOuWpm/Wa1aphVVb8gy++4NMHp4TrWyk2KcAYtNeszwSM1PuBWFaookCycT3dWI1F2cAf/fyn7FI++9HHn1AfNei+pKrBaDuRr96mOodCSdcBYZcOEaq6kguskxkag1KW6CEgjgpgc3REWZZ8++23MlTUNFRlJVJ0gyUQWW9WxJjRfEIOEqMiRC35bxStRB8D/e01atGWDkGYpqSoFwhBqvz5GVoL7DcDf16+fDkJvZ4cnxC8x8f90eTDwmSuKWR0ZHYCueq/l8YiswllGuIah54xWWv0oovRDz1+7ClTXSe3FyVNXvBEaI0ibzJx6kh5J4I9GkVhTUI8Jrm91DkZ3Ug7SEphtJHnpaG4iVr+PY4PAtGolELbYjb4pZXDvbv8fQYPb9nlp6gjOZZDp3mP0cdJg2IuPE1/Wzx52QsXVF1mIJo/ZBxGlNI8efqU1WqFT1BZtdOMwXE9yGx9ncRdyrKgKgu2tzcwjMSuZ2UMtSlQo8PHQLSG44cJxqs0Y9ptSY5KF0Z2IK0pyoIXf/slP/vqawD66Hn4+DHPHj3Dd06EYtuWf/zTnxKDzOWLExOn0DQNxhYcn55QlyVN0jPA+dTr14SoiGlxF0WJ1Yr1Zs32dkvb9TSrFduupesHlLHsdltshvymboAbPWUllfagIioJ7+52O4Ibp8Gm9dERWhvKqkapmacidQCnIaLMqXhzc8Pr1685OzujLmfFpsP7CnPhMBv/OM7G9y6oNUBVSruzDxE3OLbbG6KXITCpHWT8gtQIVJyJYLXRhKThoJQUDHXS8wCIOonZBI/ywhZVFgVmYfAmkfEC6Ah919MNPWVZzb3+9zg+CKcAi45cjItdW453hvVLg09//65dfjb49ATmxbG/RuJbo4TluWWcTiYIkUq6I48xZkfx5ImwKX/77atEwhEY+g7vhbjjwQMhVm2ahrou6S+v6G5vKLqedblihWUMnmA1qrJ88qd+Sz64qRiJqKAwQYMJaCMFV4/oTJydn3PbSaTw7etXXN7cUJUNm3KNLQv8bkeMkb7r8EOHD57dbVZkKjBFwe72mlWzom4a6tWK05NzPA5TVBKhTE1+ifyaqqZrO3Zty9HRhgcPznn16hUxeLq2k0lNmIq1SilJC5woH2hjiBE2myO63S03twLrHkcnvI026x2oBORJ5LdRCoirNGuxWq958+YNl5dXPH3cU9VVKgTrO0Z+mBZkBqecPuTQfSmFl19vjZFxdO8JvpdqX/B4FyQa8p6YhsFikrOfFlwSHw4hSkcBgaNPXY4oMOxSzyxOef21bYuCNI+S2J+1zHGUVSWtyfHXLFIAobC+L3yfQ/K80y9e9BaDnacRv8Pg3/Ee8tnMKcvysb3fBdWWux06UWoppXFpARhjOD4+4fT0FOccX3/1DbtdJyFnUbJeNRTGcH4mTmFztOZoveb64pL2+pYzZah8QI0jZgwEK+PO548eAdBpiRA0ShyR1gSVdtwQ2PU7TGkpmiqdj6btO/73//v/4p/707/L2fEJptKcnK55sb2S1MEYyjK1JIPw/91eB8Zux+1NSVnV3NxsMUXFydkZSpvJs1sjdY+yMFSF4eJihzbw6NEjiIHnz58zpCGsfA13u53UFrSmqhsKXUrrUBuqpqGuCumQIJyWUq+RqCj3oLTS6EK0N7TWU/rz0dOPGIcBrRX9MMjj0YsoTwJAZVBTdgrLrhLMfA353/xY3pSMMQzdDte1DEMnHQRmCHPMtPTT8vP4kNWxIgqhzlcJUwJxooqTtRuwtsCWhaQYSgy/LAr6MvEmOM8wSspkrJkdQ1nRrFZvWeV3jw/CKSilxMMtDO6+tmqEiS7r3Qaf/v5dBp9+UIePLc7rnlfu/W2OVtSCNCMSwqwI9eDBA05OTrm5uSGG1Mu2BUobSm1gtaIqy5nj31qZ0GtWEKAoDTpEtBOG3tvtDrs64SgRq3ZhTAi9ODFEx9TOU0bTjY7WO1yRNBIKDWXN6Dr+8It/wI8++YzPnn6E9wP16wLlPFZHgk8Tet6JbmXwhEQos2tbLq+uMUXB5eUbJt4AQCXori0KCmsxCr7++ZfUZcnZ6akQhGy3tLt2ulfaWLwPvH71ivOHD9BKdn1tArouKWy92DUjWplERKPFmJTMoVhjiYPMo+RC79n52cTv4J0jZANfAMaUUnt6mG6hjym8EPtIyfzfHpBpHAijwJP9OEBKD2KQzxROz6xToSaUpAxOyXwF6TGlFFVdizAQ4JzQC3RtlplT9GOP8yNlXbBerUVCLkWDQ9/jgsd5jzJ6gZ/57uODcApwv8HDLxfW/yoM/u5zU1FrgZvI1fIx6QAYW1AUJcdHpwCcnp4lbQIHUUg+QdN2Iu0lSkoFNhmt0qlw1ZT4BIjRQWG0cCF0ww1mHNEZR9A5lNFoqzCFwUeP1VY27qhwMbAbBmIaoApWo7Th9PFDLl98yxfffMmTRw/ZHG8oyoK261BRT/MLwQveXscggKmoIDpsWRF9z9XrnUBwc7clJqeYyFx3bcvFxWu+fblBa3j65BFv3hi++VJ0JbquZXO0oSwqdrsd5U2FNaXk1FpTFwarZydrrE49/EhW6FJKeBpk95ZORt6l66amLB5xfX1DnkIpimKSeYMZl5A7EBm7kFOGiXMh4RYOnUIIacx5aPGjEMIqJD1wybC1VhOyMMYc36ToQGdtSZXSJ5nUzMrfdVPhfaDvu5QxaWG2GuUzLt5ccHx8Mp1/WZUIcW4vXArvj136MJxChEUu9+45BngPg7/vwbcY/PKxyeCTVmC+8cvW0LwQlnUNebXWikDOj2fxlbzDlUViSEqMS0MqMGpkcnEif3Uem1ICGyNWa0xUaJ1CeQVtCPTZiaYxXKFxCBjmHU0plai7tqhqlkEXWG/J8dkpcQz8nf/v7/H45IQf/OaPef3NCy5fvpRxdmR6bxwGCm2JRovMHRE/yuRlaYyI1aYimoqKpipx3uPdAASO1mu22xsu3lg26410YdKu1rYtPnhOjk/YbI4IQUhni6JEG0PfD2iKCZFp4pxqEjNMOkOW/ZxqZhhODEk3AUbnKZzoYMI+ScuyZpA7D9lZ5Mdy3WE5cZnXRQye0o8YpVCFxo2ecRiIMTky9gubKkKZRshj8CnykXTGaJXEX+T5LkGu67ohJuSqTSPqIrlXJP0N0rkmqL0xAvT6BficPwinkHdemFuEy2PPgA8fmB+dXr98yqHBH6IiswPI53DX4FVS75k/a5kyKEUqLMqAj9az2s/SuzVNA8gCcF4WqDaK7qbF9x0PHp6Th8KMNRhluHz5igqwyqT5iIBHFoFZrbi6FhIU1g15QjOm0FIrjYpSZ2h3Oy7fXLB5It0KUxboCGNwxNJQ1Q2vvnmBcwOPTx9Qrxox8JDzZfkuRitEFjWCAu9H4fTQFhXma6SV5OjGCEWc1kLt1idewS9//gVd32KTofRdR7fbsVmtBW5c2BRByfzF6EbBZqTrOahRuBsLkUvLxq+1IURPSBOBRYZdB89msxGOi+EmCa3MFGhLp5A7HMCs15iEWORaiPaj6GR4vFtwO8ZAYQ0qeEYndRhrNMoKn8G4EJiNIY36oiS9iJGA8FoqFIWtsItlHoJPPKBO7oUxGFOgtTBp20Qtt0xnQDabpmnujJG/6/hAnIJajE7/ccL61Itif/Yh//w2gwdE25G8oO8a/P5jaUeITO+bBV3yqGyMAkLZbDa4tJOXQFFVEoa7EW311HLy3tF3O4x5hE6eXlnL6EXAtaxKlNEwJg1D7yi0oVyvcUPiX1iBKmR0VxmFRtSnDCIsYqKisSVj2pmJAbNKQB1rcD5QrVYoY/j24jVlgHgwVVcWJTFmQE+YrqFWOqlo++nGuOjY3sqvmW8CBdE5+uATpXucIpGT02NihJvba8qqJHrHMPYolYFQUhScprGVAiN8iWkGkRgTZ4VOczQxF33BRzURwBZlOeXksyh4XjuBbcI25BpD3/d47yYVJ4GKi4JTdA4V0jg6QPByT7wXcRaS0I8UPaRjMffVGd2A1UIkI4S/hhBDKj56waVkGrVopF6lNNpIFBEji+cnMZnpEqnJLC4uLvaiiO86PginoGAih3hXHv/dBi/PnRaslhDykB8hP2d6dzW3pO4z+P1e9kHdI87vYa2RvDZ4TFHMO2diVpbR4KTSzEDT1DTrNe3tFbYuMCm816XCEXDRUdQFEOjbdiJoLa1lvdmkdAQ6JC8l7UgoJci9IAIiD8/O+a3f/E1++uo5ANuxx/hC1ItSBb9c1bhuoHeOzeZYqtzpexfaCMNQwofkWoNK90nFNFGZSWWCwvlehHMnUtV5ejAi6VXbSSGzLCvGceT2+oqjzYaTkxNKaxP6UHZUk3gPSN+1KAQfERP2JPkB6flrM7dHEcclYimW9XojmhBRQFtaz10FrRRtK6j9GKOQx0bP0A9SMNWK25tLxn7AasGTZGXnsrAEBO+h01RkTEC2fW6JVBfRJkVDVngmvCM6IW2xVjgSQvBSQwCUUcJclVIlfQ+z1QAAIABJREFU58YU6QSClzFwbc00JUlMSMnEevWLqE5/r/vw/fH98f2xd3wQkQKLMG4/CoC3hf73RQGwHwkso4O7UUAuELH3WXv/3tPhWNYWYN41824jsuOZIFQub1mUXF1eYYyRNtM4UBhLs1qjiwvMcUNxtkI1qeVmIi6MAoAiSLtRQ2U0Q9QU1rBuVlOkMFhhEaJI+gRIcTA6h8Xw6MEDNqcnuHSuX71+yW70YDVRZXUpiWKef/uC080RyojcHECBhLWFMWT5O5+um/eDqB4tboKAcNSkaKQ02KIkRs84espKQvi8I/ksIBsir199S7Ne87BpJn4CAQllpOsMksqcjkoXGBMIAQJCjronq6cVQUser3RkdL20K0PEIKE4QToLXYoUnHPcbm9ROoGaRo0KgbYVgplqvZbWa0ZAeqld5DBeKQgqgveJbEWo2jIJikm5viaRrwy9tFStpTS5FhKn9eVHnwSGdepWaEEyFrlYKmLB3gtgq7AWk6jpjDGJ6+JnvM/xQTiFXPU9NHjYLzx+l8Hn98pG/y6Dv6/DsVdMhIQ1lz8cwmHnVqnc5KoS4pEQpFj0+vUrThJl+ziODOPIcHvL7VdfobTi4YOHEgZbS1lV+OAIMVO/i7yaCpFxFNEXq00KjaW9tN5sJlj0rU6U4dqAloUXlUpjxQqDolCaH3/6AwDKuuQP//EfET2YskQbSxg9zkdMUfDNi+fU1k6Fz+ATNyGKrh+n/FopqfZHsu6BXECthYhXW0s3DDLoU6nEPJaMRutpViF4T2kMddPQti2vXrzgeHNMXTWQqgY+xEnTAq1QXvgajLHSqZmKvCUxiA5kzscDgZDqISjFrt1ydXlFXVU8fPQI32diEk+fRryDD9RNzTiMtN1OjDkKxX5dVtSFZRx7IdQFQSpGEGh2XqtS09GJnzEyw6SdF2Ja0nrXRqEwE2Aqp7cTlb3xaW15/Dim9Su6GLaYwVW5Q5Np5iZuR/v+pv5BOAWBt0q+fN/Of98uf7/Bzz8fGv2hwU95cfqw/cgkvUsMUx58UEJInzH/ZrSBQujTnXPc3Oym2fZcCLq5ueHLL7+kqipWqxVN06CUmYg+6tTS01Fw7KWxeBdQRvJyFQLeGLbtjtV6PeXYPlGCCTAmzdYHL04T2eGi86wTruGzJ8/kfNotP//mOaOKKGXQ2uJD5Ha7pVytp1aqHoRLMEJa3HHa5bP+pUmtL/m+0rkw2lJEzzgGYgLP2EKnIpqaCoG5y3G03lDagq7r2d5uWa82lFWJNYZxyTGYamjKpO8ZPWiDUN44ClNQlGaC+99utwxjjxs9Z2enrNcNz59/Q9e1PP3oKdYb2lbuUcx0aW5kVa6IJmDTwBHOURSa0mjc2OPGniI5nugyu9dco8qYiLzOlDYTTiGEQN+7VOxU0pb1ohjmnEzciibmzKuplUmgKhGqDYlZOkPEjdGTFH3GVKhU4Py1cwpTz/leg4fv2uX3Gxaz0X+XwS/f6/A9l58/T0DO0l+z+GmSVg+e3a6lqkQ+rK6EOjyfYAyBfujZ7rbCUtw0GGNlMSQ+vqwhYJUUE0tjGV3EFppEu0g79AzecXR6PC0wWxQ0m2Ncabi9vmHwDpf63kZK86xMOaHadDT89sc/pHUDrh358vVLRjdiNseCN8AxOjcpGKOS3YaIsaU4aeaoTuTzFjchFdpQMpAVk35FiMKeFL2Xqco87BMlzO67Djd6NJaxFxHXvOAjA4E8QOVwRgxSKy20cqZAaYuOsGpqtpeihQESxd3eXvHmzRvqumK9XlEUhuvrW0KQ3dhojYuzgI/cF2kragWltWgtxWSDnK+OAU1mgxKiVK00UZmp6KkSS3WIkdE7ulQIVBGcG6aFlwltM1W993JdMxhJoNCGojQJDSsycRmHoLQSJx5TR8pL1JOdwZ8YHduv7Ihx2u2+M6xXy5RC3YkW9nb59zB4SOGbEs4+MXidPmeh/XfnfObHxStn/LxgEB48fDC5MjeOaCNqR+fnDzg6OuL09GxiI2qqmoIEeQYKNAbNZrUCIlFpWj9AgC6CqgpWJyf0aVdojtY0R2tWx6foqLnoR8mtC0sYRoIPWNS0c7rRU+oCW9b81mefowvL1y+/Zeg62QV9wHlPyMM+izBfGUueNpA2shInEPXEjBTztfcK54N0CKKiKmq6rpsp1NI1jCr11QMYWybQj5uuHTGAF0l3AEegsAoSa3OIEW0D6ICxkevrGyDSJwOs6hKt4fXrVxSF5aNnH3F+dpro0iSasoWlbcPUJiUEgnN4P9D3LcoWrEuD/f/Ze7OY25Isv+u3IvZwxm+4U8419TyoXdCWhcSMkGjz0sADtF8QEqJBAvHCi414sJD8hrGEBEYgLJsHaJCMECBLDH4BIaxW2900De6hqqsys7Iy7/TNZ9hDRPCwImLvc77z3Xuzhs5bpRtS5v3OPvvsMdaKNfzXf1lRywQfcRux7BlPYQyFNdgicj0kPEFsOSf9wAGpJdLTmNZNjXBMNv8T52VSUiGIkgQHj6DKQgTarouxm1gEJ+l6tJJyXMX7quP1UArArsDvClz8K1oQAHcL/O5vyGZqEvCxwMN+6vOwwO/Wzg+uTHJnQFeYsixzC/Ttds12q0CYST1RU68sefuttzg6PtJVodRVwRAwAehT0CpAENoQ2Iqnm9SUhaVpOnrv6a1wtbqmOtIil6brOC5KnHf0nTYFsXWBtQXOt9rbgKFgaVIqJXvrek5mS37yy1/l6OiE3/57v42dTjSwiZB4/wIxFWYE17faI9GYmFvXtGHbbLNLpshOcDFvLsYoys8Ik8lEV8LeDcS2RsBYjW14dT/arssNXYNAcI5UTmSjHjKEHKgzPmCsxv2C99RVSeu7+DwNdVUSXM9nn36X+WzCycmxgn5KS9f1FLYYej6gitAHh3e9cjn4HjC4vo0UZ0qem2JKIhpo7FyHD0Y5KkUFrI2oRkzELaAxhbbZUleF9nvoWtRSLtluN4SQKjETFVyBd4aua2OTF2E2m8Wy/GGuFmV8RjZ1okockokq/uXjNVIKtwV+2Db+vC/wZkeRvGiV3xf4dLyxgB8S+PjrW8dKgU8YGp1qia1ns95yEduuPXzwiNl8rsHGiFfYbDZMphMuLy85PV5SFhWpeYwPQu/BFyXlcs7TzYpjY5iZQI+waho+/Ogjvnw/BjIl8OzsDG8sl5eX+tCcIidd8BqgFCEklFtRKaoyAK7nZH7EfHnMb//mb9FuWqzofaQ7FmMBj5QWXE9pU+AMeqdKCBk6PvU56Ka++BD7GBiivQtIhOwZUc4H54Lm2SM3hZHYQCV2cCaumt4HXA+9OGykPjcIEsD4QAgF69V1RvVNYwxnNp/x7Nkzzs/OWC7nnJ6e4kOkjzeGo8WCy4VWbm43N4S+I7ieuiworaGIPJz67olNYaIV1Xu22y1ds0Vsoyu/MXgC222TXac6N8rxtJstVV0wm07pI0AqVIHr6xsmk0nuNxlPQAgmdrLWmMNVXICSi+C9R5pdy9lazUokoNurjNdCKajbu6sMjBmE3MS3MFYAhwUe7l7lxwJ/yL8afrcv8Om3O9ecI5DD6GNqraoqyrLiw9igdbk44r1332Uao+ubtQYh7VXBp59+hi0s7xmLTwomBLbOEeqK5VsPWX38Xfq2Zd3pdTnxXFxe8E6iC8dz/tln1IsFfd8xrSo6p81FcGoSByP4CCve4nEWQusp+kC/bilmNfeOT1hf3yg7dRgo6oPvMlWYtUW8a3UhVEhig9e0ajoHqXrRGIjCnaoSJTbPSatg2zVqBgfBeWGxnLNYzDXASgK2DSleH4lZg3d49FjinJrYsejo6vKSbQT+VFXBYj7nH/j6L/HRhx/x+MljVtenPHw0Y9vGdx0CZex/CbDZrDFBi5qKsqCqir2eJKmPg27zQSeOsWXMKNg8r4HcMChNpKqqqYqSsjTUVRVdUAVePXjwQEu3EzhLj4B3AR/U2greqdIfw7QJOY5VFgV93+FD0Ga/9geoFORw27i/CPxrwNO4278bQvhb8bu/APyrKL/Hvx1C+J9ffg59SHcJfNrnZQKv24e0z+jXO8cZmJ7z0Q5YJ7vH2NU5t68vpVUVU1FQVTVvva2kKmdnz3n7rbeo61pN47bl+vqaths6+ARjU8KNzns2fUd9vOSn/9QvcnPvHu7yEn+1pS4sneupZjNCjHx3fYcXKAvLdLJQ4WwdtvWYmOvucPhYJdmgJdYhpE7LgdXlFV96930++vDbuLZVpF7OwUcyEEmRbWU1Sj0WnVOBlRh0Q2LxkA/AwEEwBA11JMtCjMGWJXU9oaqmzGdLzeGbiI+oKkWJpsCtMQTfawWgEeax9LzpXMQiGKqy5OriHIBnj58Q7t/j3Xfe5f333kOAuqo0chuGLgrGCIvYb7PdNlijVOvWAE5wKPYi9ZkMQg7GGoH5bKacDY3LjyuYgTqwdz1Eq6ksS0wIFKWl3W4jGDTE0mdNr2oqPNtruTQ/BST1zSV0p43VlZHBumvjOxho5V91fK9t4wD+SgjhPxhvEJGfB34N+AXgXeB/E5GfDomM744hIjspk52CpSz0t3nx4q9vHWtXqbxc4PNR7hD4F42hkEv/ds7pywmwXOgEOz+74OnTp9y/fz9HmbteOwM9ePCA2Wyuq2i8rM57xPVMpjPe+dKXmL33JWTT4FcbpvWUy/UNxdEcHwk+q7iaFVbjMq7rEa9NXzDaeCT1SgSQ4LSLsoTY8FRJRJfzOceLJaury4jlT0ErfabWKJiod4627WNlnqEsrdKxpQcdKxbTu9CaBENd1Xk1K4+OcsqzqAqqekJd19hiEgt9LKYotJGONUgBJraZK8sK1zdc3lyx3W4ojWUyUYHUMm+JXbjUVF+tVhq7EcNiecQ7b7+rEO+gBCeBRDcvuYiqdw6DYCutWCzLUuOdMuJEcCHPl4CS1yr2wBE82cUysTeDb0JWhF3T0rYKo47QBuq6xvlEWV9EGHMiaU3zTNvbJ5q41EgnTeJEP5AVOYmp6gcYUwgH2sa9YPwq8BtB+z98S0S+AfwZ4P968UnQEtMDwrq7yr9Y4OP1Hj7C9yHwty42f7+7n7L1GLbbBtN1GBtxCiHw5MljLaUWnWCzmeCdrtg46JueqlLT1YphOplgS236wnTBZLnELo8JVY1pZnQCTepHUGgVY7PZqlHvHFYMVCW+1+CjFZNr81vn6GJa0DmPkZ7SmNjTYUqzvsEwIAclsgEbDH1iDIoMT1ozGQNvMXtSTyYUtqCPQbCyUJN6Mplyc3PDtmkIQm5nd3p0n+OTEw1aYhEKJW+N7ztEd640iuAsxGJFLZTvfvIJwXmqeqL8DvE3dVVzeqJVoUYuWa1WfPLJp5yetrz99tuqPJ0S0zjnBxc2rrRNs4XScrQ8obRaUNWj3aWCU4ukaxs2YbCmtMV9TVHWWDE4NDLetT19pxyTfcRbdH1H13YUpeXo+DjPJy3VDpRlbDoTUatdr+7VgOx02d3wLqgiCgPnRFEpqjOhaBOD06uM7yem8G+JyL+MNnr5d0II58B7aG/JNL4Tt71whIj42CU+G1KMecudAv/5hP3wscLe97v73QJK5dRniBNKwSJVVWlNP5JZcyywXq3ZbNbaYt4WnCyOOH9+TiEFzbphdbWijv7spK7ACJebFeBZrTqmZU1dTqh6h1gwhc0dolwEJxkxFGKwqUKyUtKV0lpoO6rILu288vx10Q/tnY/IR71WfEAsGZ6bynwDnrbrQCyT6UxTsN6xXq/wDpZLZYLqe6UhW2+3XJyf8+jhQ+4/fEhhC1abZ3z44Yek5jkAP/dzP6Pt6GazXBhnC4uIFk4FGUjXQLMaZczePHnyFNc75oslR6f31A01WrqeLAVQjsLr62uapmM+nytxrhjKUmj6Nt5niJTxah253lHXE3zfsd5uICg4DZRzwxihtIojMIlKPcUOjGCC4INTuHK0htP9FWXBpJ4wnU2p6ooudv9OXJDJ5UqLYNtq6rGsyug+2GyFeR/p4WMrPNDydaWZq7BFwTS34Xv5+F4Lov4q8BPA19FWcX85P/3b49Byi4j8uoj8loj8Vte9ekfcN+PNeDN+uON7shRCCI/T3yLynwP/U/z4HeCD0a7vA9+94xi5bdzRYhoOm+o/KEvgsBUw7JfckF0r4LaVsHusRCSbACjGaK56Npvgei3+AWVcEhG++91PaVyH84HCWCZVTV91rCNfoY+Q1ma7xdQWh6dzraaqrKF3Qm9Fsf5dHyNgamZq4NBTFgYTdNVrN1uKukSc4+rxc4qFmqnToyW9Fzye3qmV5rqOQiSDdhCDS2CkWPtQ1xMenTxgNlsocUeAm5trzs7OCEGyGfzkyWOunp0zqUqOjk6UCKSooiVVx8h84Ppa2ZnPzs74oHmf6XweLYSEsQjaIBe1UhJ5kKZY4Xh5zHw6pW1aLs/PEWM5Or5HNSl3SEXKsuT09B5X1zd89vgxJycnVJNJNL/L+LK1p4KNz3SxWNJs1oqEKCpC34Lvc+s9bOohOpT8a3YrkrVGJmpCbAwT2ZEykWtIuILYMFZi8DFaCtr+bvce9Dwmn0fEREKf28F1Tf9q+lIbDP+QwUsi8k4I4dP48Z8Hfi/+/T8A/5WI/IdooPGngN98hSMezDjcNV4k8Pr9eL8XC/zu8e4W+FyLEadoch3S8b3m3/CR7FNr/qMpLCVFWdF2LevtmmfPn4P3LCZzuq5lvVnR9y0PH6oPXNUlvu2wpfqlfddyud1SV1WkVdeiqETKUpZlRBYmmvBYP+ECtnNcPHnKH/327/LgSOnhfvLnfp7p0YLOB1K8OoR4zz5gpaCwdexopVWMy+URx8en3H/wkCCiHacRFstj3nr7XdquY7tRd+mzzx7z7PlzqqLgnbcfMV8sOIoks8vlkkePHikfYqzFsEUB1iiEuLAYKSIc2kdAVMAz4CYKU+C6ltlsxpc++BI3q5WyQdsLqmqisZNiIDQJPnBy75Tzywsury5Yb9dsm7XyYqIpVN931IWljEJ+vDzmyvuM3MQYkCJ/n2oPUiDQD5MOEaOUeyTyIBNjGC7HzYLXwrXOdREuHvuRxn2q+GxSal5i3KZtu4h+tLm7VcIqjNPtPlbXdkEb51Rm6ND9svG9to37J0Tk66gUfRv41/V5hP9XRP5b4P9Du1H/my/LPOyPV/P1w61/d79/QSyAQeD1/gahf6HAp5cZf29MxE7IgJ3w3iugJeWYwwjh5wJVXVH2LX3X8fz5M878cwiOy4tLgnc8iGCk+ydHlEEQDxbDarPmyaef4pxnfXKft995n+PTUy3NBcQFdQRDwLseF0SVRtdy8/icD//e79F+8oRnn2rLstUnz/hT//g/wuJkQd91NOjvdfJacpu7SIJSlCXL5THLo1OMrfACtihzOlWJPDwXsSXavfsPuL654eLsjI8//g6L5ZJZpFrbbrdsmy1t03J6qvc7j8Vdkp8lIAZTaLekgHZayhM+GJq2QQic3LsPCBdXV/T+ktP797FGuQlTkxRTljRNw1tvvUVVaxam63oFdAXB4LEhsNmsWd1oA9j5bIJhqRgP31PZAnDKTxkCargMmYjEa5SzBAzxHiE1kxlEIVHBKaW75KzCuO+E9z5bm6mrtTGSKek1EKzFbFGrQ4i1GDZk5KXE9Oarjh9o27i4/18C/tIrX4H+igG8FLLZpZ9G/38FgYeUQkNBJHcJfBiOmTkauVvgByq4vVLtGGHO6VLR1l/WGFwOWkFhhSCGuq548OA+vndcnl0wmUxYLOa0mw3PHj8B4Cvvv8dkUmMDbDct5mLFxTc/4upizeWsZvPecx6+8zYnUYmcnJ7S+J7JbIothLK03Jxf8PHv/yFPvvFNumdX3KtmuE6F6vnjT3ny3U95tPgaElBIb6+BrfOzC66vV8zni7xanZze4/j0PtPZEmMt00md0Zs6mXV1PD5W9uqf+ImK+/cf8PFHH/Lht76hUFxjlHHaWpq2o+t6ZrHvQ11PqcpKm87YQhmRbIn6RGqOF1booktws76ib1sIcL1acX5xyXa7BbMlxE7XzntchI3b0lLXE4wlKpZhVXddz2az5fr6iqePP8s1ONbAYhb7QzggOPquV1BlUOxHwl3ovBhcgvE88SMUp7VFlk0XKajLoqCoFB7fR+5IIJL6DqSxWiZeIlIgEiL8W3KGJh14DPoTEYyL1ZuHQ3sHx2uBaCSQUzWv4sffJfB5z5AE9QUCn3L2kuoiXizwO6QsO4pg+IxIRpQFyK2jAh5jLL33A09ggOC8+qWTCWGx4PlTxYI9f/oM0zSw3bB+9gx3cc1XNgaosdew/v1v8/u/94e8+9UvA1D8ws9w7923mBUVzy/PefzsOd/8/T/g+Xc+ob7Z8v7xPWpvcLHYZtU0/PG3/hh7/5j6wf3YyQi6znFxccV22/DBB1+hrjV6f3xywmJxhC1KJvUEW1ic7zHWUBYVXWw1nxiS58sFRVVRFFYh3GWF91BVE05O71FV34kMxvqgjRiqssZ1QSnyK0E5F8EThS8oPRrAan2jxVlO6xOK0mqlWNBjOufo+h4XV1mv2gARS11VcbV3IIHVasXTJ495+uQJm9VN7iplrIDX0ubCGq0pcS2J+9Ay0MOleZQWtFRrk5rFiFGNkVyKdM9eYkWw13ngjaaIh6Yzgzug6e5hfnqvZDo+ksPkgqd0TYmzMUSeTP8jphQCGli5JfCQhT6MtHtQSdwR+ORxykjodwV+x0DYO54f+BjvEPixktqJf+SVYmAzdn1PsDYHoYwReqc8C9V0xrrZEnxgMq2QoLgEPCwi7v43/8+/w0lV8rAoWfSeE1tyv5ogIvTeMSssoTJcf6Kci3/Ubvlq81OE0vJH3/wG58/P8TcbJmJ4tDiiRpQLIOIgTGG5vL5UZqHTI5z3bLYNvfe8++57TCdT7j98FDkhYVrPEFsgxmLKmoCLz9mAgbKuqCeTERW6YzqdUr/1DqdHx/Sup6xqClvw6OEjfukXf4ntZp0RinVd03aOpr2hrurYGEjQkqdYth1E+2YAn376KVVRUhirmP744FPfCdf1dJ1ju9X9222HEWFezChsSfCezWbLzfqaJ48fc/nsOav1DceLBdNJxHK0W5wbio0MgaosMdF3d96joYbIIRFrTNKqHQiZLCjxTKozRJ6nJvJI+KDdrxNMHLTQzJihoGnspum0TfEjR4gug0Am3HU+RHSnpvr95/DiXwulIAw04rsCPwivGZnxdwk8jGIO8cOLBD7vORJ6GSmjFHgYC3yyKBRXofvsWxkhEPkI9DcGSK3qt81aI8GKMYpIQV01ju+pO7A6O6PftBRFxbQomZsS2/ZYFyhFMQjHZcVFo9Rhl5895ncvL2hCz/qmQRAeTmtOpnPuVRO6lebYVxtlKm7aLe/97Nc4Oj1h1WzwGLbblqKu+fJXvsbx8TFtN26JpkJaFCXWEAuk9N5DCBSFpSqrrAS194IQiviQt1vKSvtNVlXNu++8S+86zp6fxa+3PH36JMOMJ5MpRVFpnYWojRdk4DpYr7dsWDOtaipb0rVaZjydTfNKDoH1SuMDXeeoqoK6Lrm+uWZ9c8PZ82dc31xzfX0Vi7wE71o2m4T8C5GReViwiqKMYKqhBHoMuOtjM1kTtBDKxqi/630MWks2eotEQjtyS40YZak2EZ/hh8qHNO+JS6D+63MBICjIbHCLIxVeQEvTf9T4FJIA7ws8sCP0twXeJSvpliUxKIGh0OqQwOv2Qbm8SOAhKqugWji1TM9KJgTKWhuYuKYhVdA4sbqoWkPrNGAkGFxQOLIhIEFoIxR1sVzQ9peayux9XF8CpjCUZU1dFazWa44izLk2FWvXEbzj5HhGRcGCgtqBu1ljgxbsJPai+28/4id+8idgPuW62epKVZYsFhozEGOo6qGPohY1CYUFY3w0V1Od/9ABPKXzEngrCJlPMKQ2TmjHphAqzs+1NuHm5ob1dkVZaQfl4+N7CqEutahIV0tLVWqM4+joiMeffcrq6pqH9+7TtFv6vtMGM82GptngvKGJpeur1RrvWj7+8FtcXJyz3a4xRrM2k6pQZKlzdK7B+IgINOoymPjutUxa5xMRGKQ9Q+JKbgxdGxWKVbh1iHUK2oxF50rOJojNE7v3GnTsXUIpxuduyS6oyBDD0ZU/jKwG8uI3iH5aTAtsTGe/6ngtlIKJq9C+wAO7Qr8j8IPPBPsmff4fCRV3SOD1+yj0PuQXd5fAw364ZvxJrZi2aYb27PENtf2GqiwpyhIh0Hba8CN4j3joAxmSCjBbLqBp6b2j9Z5tcEzrGYjQSqAqLa5rcUSWHROY1hUnswWh65lgmXiD6XvtOg08ub5k+vABAB/8ws9SHC85bzqCF4wpeHj/lHq5IIjk6xhbT6nDsuRYjMnB4RC0H4HYZFn4HDOJLzhX81lrI+V9n6nG1usVV6tLisLECdwissCKUSh2UHO7rjTGMZ1Ocb5ndXPNw/v3OTk5ZjGbMl/M2GzWfPjhNdPZPNPhXV2csbq+oIvdqooiZU3AB+XGDMHtmNmt7+NarAJPskyR0SIGZtSIhhRbijPZOx8tqSLPJZOtrICNyoPQKyqVgAtK3OKjwknQcSP6HlM+pixsnEsdwu7Cld6ZZjJevToyjddCKWiFYZdNnkAYqLriPrdXeX1JJsYghu8HuPRBgXc+Y9LTuZPQv0zgQV/OoFxkR9mAxhNc31HU01GOGXxwNO2WEE2/3jssowARAVtFkzJMmB8taZ9fUAEtnmvXaGuxwlJ7w4ae64hn9wUY77Cx8q9xDhccXdeycYHrtuNrv/x1fvqXvw7AqjQ8vbnhpm2ZTGfMl0cslkeYqlKbZC+4mkhWk6szzodrDEiFKU1gpQWLQS5ncTGXLiJKpAI0bZN7GmybJroAjqsrXe0RJVtFbLYck/vw8OEjAh3bmxXx5ccsAAAgAElEQVSPHjwC59iuN7jecXZxjhihKKtsKTTNWgO8xSi3H2Ib+1gYlpSdjylB17uMMVBrQeHVJkQi2TAESXUe+Wzdjn1Z7xxdihWIIH7Xt1fgUiyRjgVn0eGk6xxuFD8s0BhOcEpy3GtaRLkfY/Axz/34zEkh9lc3FF4XpRDpuMYCnym7GIQ+CeJom1a5DULuUwomRqKz5bF7xr0riLGKlwh83HMUX9gNaIQQMGWJLQolMYkm22RS4fqW1WajLELG5glk4rmcH/oS1kXJ8vSEZrOl73s2fc/at8wWC9774H3e+dIHfGUygcjf57yjbzv+x7/530EPR5MaYlPYar7kva99hZ//07+Mm+rr3jYNzgiUBZOjJcuTE+p6Ru+HxqkhjBvsQBiZr9rU1cfVUwlctCFJxPUXiVTF45xaBkjqaxgi2KbM7dG3zVY5IgvlMVitbgiRq3MIPIOPSna5WODDQ9zyiOPlEbhAO29Y3dzw/OKMruvZbNb0vWYryspSVZGeHo/zfQzC6XEDIVYy5rVI05c+gGgGRIU/BRHJ7zz1z+w6lxVkF+sjFJ0IvXfZPU6IhsTa7IOe2wG4VCvhtSR9FBhPDFMqKx7osmL28b2pNZeCjslVGdyPVx1vmsG8GW/Gm7EzXgtLQYRIbrlrBQA7lsDYCkgVdretADhk+h+yAvTcgyXwMisgHXocxCQM36WVLbUtSyv/dttQltrjoXVaylwWlfqQaO1/17cxsgRioOkd7mSBK0vmDx5wsljwwbtv8+j+A6azOb01uKj9Nzc3VEXBwy9/wM3VNa4PlGXBveNjvvy1r/Hu+1+mrSxnN7HWoG1pjbA4PWV+ckw1nWGKAmn8KIuy22Y9PZNAYhGKvS3R9mQShqCb+JSmU9PVQyZS8X2HiDCbz3gUSWhm3/oWzvX0bks1r7XbtDH0XUs9nVGImsJdbuluWCwWlEdHTKqKdtNACGytZVJPWK1XrFarmNCEuiqwBpxro5kfY1Ix9ikh4LDqdkSR8DE4Kij2RFC8gVoJw3NKMGftdanZBmMjNZ1AAkuN3bE8S6MbJhgtNccp6Mr1Q9/INDej29Z1WrqdSGusTdWmfof6ToFVbudcrzpeC6UAHBZ4Dt3MCwQe7jT971IEO0I/FvgMVgrj3eIVBG5tjENRjI75Yp6BTE+fPsWYKVU1pQgaHPLOU8Smp20smy3LaLgZ6CXQGfC+59OzZ1xcX1FNajoRlk0Ltf4NEdNhDP/wr/wKZ8+e0q43FEXBYrFgsVzSmorHFxdctepjd3VJOZlwdO8+09kcRIOiiVQ0xD4V6RmZxJgUQobhJqWn5mxPsGG0vzIyeTfEHWQ0uTUI65hFvsL333+fi8s5m801905PmU5nUd9qGhcRetfRRndAsyB6/tVqxfnz51xfXHJ1ccmm2WoEvnfamBeQGF8JeO2cFVw2uVMePzXZSW9UA6e6TWLmIcT7DoSBbzLnDBX2rIomPZtBGYxZwnTqDDEIDdDmiLkeTDRAmd6JxjX8ECcISakMi6f+O7gUqSFMKsN+1fFaKAUlo1Q/bBhD/nY3c7D/WffdjUMeWOUPCHz+jlFs6AUC/7IRUJYgYyzr9UCocf/BAwjQNMqANJ8taNuWm5sbBTRVJZvNOmMz6tmUup7hcDRNg8Gw3a74nT/8AyZ1zc/+zM9x8tYj2njPru/h2jOfzDj90gf0baMZFOd5vF6x2V6w6TvaeG+nJw8pJhMm02mOUkuQHL8hPcPhISmLUFIWoBmFuJJmRZGKhexI5+bDjTpCJf83CsU7773L6b17EDomkwmTyQzndAVdrzdgNO7QbBWXIQZW6wuazYZ203B5dsbN1TXbzYayqiJ8OmhfBcCKtq6zCG2nqeIQUlYhVh2mFnSk20tcBT7HUfQeDV3nImnreN5FWHPseeFGQb9xnCYJsYKfYoDTe3rXRtiyj92iRGnW4vGVETrgfU8xtkRDiBiEkIuqgBiTGZT47Tja3eO1UAqCaMnswRU+7iF3C3wYBWT8C1b5713g5cAjPRzONcYynUzYbLesVjqJt9sts+ksU5tLJZEAJLBerRHRsuQuIgJXmzXz5RxTF0yKgs16g+t6Lm7W9G1HYwpOzp7z5Z/+aUC7JzfthqvLC+qqwvWdBqMkul51RVlXmQTl6ORUezr0nq7vNLKPQQozpHgZTaPR87ZWI+Q6qTV92bs+07EDFM5lVqXEmahmblIqXtOVqW9F7JZVFzaayeC6RCmmuIeb6yvOzhUG7pzj+bPPMgtSgeFoueRooffXdG1sfhIzBJVmHPq+Vdo4r+LqfVyVfUBsiJmsUVA53p9A7gkyXv139ObO3BqUwc6M2UsBhBA0ICwBnxYstJBJxIDvM5lv17aZbs55jxRFPs8YwJTfQaGt61Mq+PNM+ddDKYh2RNoXeNgV+lcS+LEN+OpXsPeTu/M3+6mdnco+hm5AdV1xfHwEaF9DZfGpqes6N/qYzWaA5FUgjb7vaJqGvuu1qWtVgrVUtqSYBD7+9DP+77//h/xiLFV+9NZbzCI/AEVFWc+YVNqHoigKrNeCoGRO9yHQ9R2rq2tc1zOdTDBljVjJ3eTHTyFAXAWVtNRFOjJTVkN/AefxsRGJi6tuFkpjlLot5u1F2GnPnjMYQdmKgg+0XYvzAbGG7XbD82dPefZMC8Z8cGyur5V9KTI9N5sm9pYw+F6vz0YKeRU1LSlXigb1w3vnMKLWjsUSzNhQUsVhrM0r8jjb4ojxrpT1CuoOpsyEYh4S/XO4NXFSPCbFJYqqVqxCdDF0e36EMSMhFIV22ErP0fn9ya6fu27gb9xnSn/ZeC2UAmgOeF/g9e/4b3y1f3ICP8Cqk8DrPuNto+17BwiBTAIKSuOVhGk6nbLdbrm5uVGyEu9Zr1cZzDOZTwmux/ctmwaqesLx8gQwNG3Ho7dm/EPvf4kqpvTKqqKuK8qqVGCOEYqyZDbX7/tth5GBX2CzWnN2fs7l+QWFMXzw/gdQpAkptx6RxPsmhLji9rRtG5uVSC5CMjbxFTqc2Kyo02+tST0nhb5rsmlsi1jmHDQm03ddbK9XUJkK53pNz8baB4+nMiVlxBFYM4DfnPOIj/cRX7wCg8yQShy/J4YA6ngMMYD4DEZFR8ZajPf0XReJbzV96cVrebSkkHnY5VngtsZNrNdd18X4Bhm4lC8wXWPQ+0tdrIbiqOTimHxgjT+oJZQsnlcdr4VS0MBN/wMQeN12cMsBgQd2hP5Ogd87QJrkqun1DnwY/LjFYkHf96zX6j6k/pLNtuFmtaKe1CyXSy4uL9hsNoTgqauaInEoNh3WWeblDG+1ecvJkSqF9bZhNl9QiOG9dyL9pZEsYKo6FTNgMDivhJ5egjZtAW6urvnOhx9xeXHJ8dGS9957B5EISJLbpmaecGliesdqtc4+d9d1WvQUC6i8d9C3BO+wJmTXoiwsRqDvWrarm0xKetOogjRiteVa8DTNluX8iL5rubm+0iYrAwaIqiy1pyPqspVloUChro9xHc2QgLaAH/wiyVgEJXQx2W0dK4LkIgyt420mM8ldoUVy/88xO7Qxkms2VDXEmNZotsrIJFMXzOsci3ENgcyNCQq7Tu0LUu1JCMMCKrLLiJ7cCK02tT96SiEZAbtjMP32t93acmvz7VX+LoGHXaHPcNEo9JCg1mMrJoxeyKAYxi/JRlpv/X0i1FCW4bOzc7qu48GDB2w2a66u1pRlSR0r9Eyo2dysuV5dM18sKeuC1c2aoiiZT6fcPz1lOp/j4sppK6tVjBInY7zJ7WaDCw68yUVZoNHuoiqYTCuKuqB1HZXvKcXuPO+x4rTWUhhD2yr68MmTJzx78pjJdIJBJ+RyMo3Xr41okvtyfb1ms1kholRz5+fnrFZrLXkG+rZVYlIMha2wpTIwuabF+8DF1TlNsyX1Saystqwzo4tNAuyDoyjUdUroRO+7aPqjHaUikWqqXxjfa3p/zmmTui4EiEoorcopgDfm2cgpyWKAf3vv6fteXTirbefHXafH5dYET3CCxJtSZSN5Dio9v4luUMgGj7I7D7G4NL+9T8VqSvFm5Lbs3DVeD6WAhlcOjbtW+UMCD9wS+pcJPAxC/zKBh2HSJLSYTq7hbyOSm6gm98H7wGa90ZcT891DPllTh9o5SmG/p8sjQjXBtcou3LU9TeuYzmeczE45Pj2mnsyyiemDoS6LYZKhfRXbtlWzXAzODzn1elLzwZc/oG0aAgFbFXgT6HyvnH97Wjagq6qmtzqqSUnvWtr1hpvLC6bTKdPplNXNle7vQ+Q80BV/u93E/xTafHFxQVEUue9D4jo01uKtwzp1PZrVDavVSiskjdUSc9Dn7QWR6Fc7R9upn22tcmV61+U0XW55H9N9+h6I6dSYJQiCjVyaaa4IxP4cSoOGaLwnzRwZWZyJkCelLlPKc6wQ9H1E3s5au0K5GP8IpFSuGawLYbAAZfdefHSRBiyJi82Nd5Xb0IDn1c3w10MpCLcEfCzcr2LWjx/GOB98p8AnMzH+f7zK3yXww3fcUhLpbxdC7Dk4pD3NiB5stV4zn82optOdgGMIgSeRealvexaTBYujY20E2/ec3r+vtGbTmfr1vst4VJFCW88bpSV3XnkA2lZbnQcjSFFSVJG/oLIUkxLX6yQqqzJPaB8CrteiocQxmSLttjDM5lOshaqwNE77GQTnaLcbbq5VKWw3GzabDevNmuurq1wMZmKWaTGdKYdlNHeTkiysBdFoX+8i9r/rKOuKqhj1tvSKM3ARGOTc4FMnjIAfd5QizoEUGIyprFSwpP43t5SCvjszmP8+xHPGiSNpBU/kuVpjE+JKnij/A0Gp4QO7K3ZIAcQISRaI4fTB2zGD0OtsT2nHYT7nquJRyC09jxC0huRHDrwkInnV2Bfy8T4vE3hgR+hfJvCg5mRe5V8i8ECkHhuuMa9COW0q0YceUmLWWmazGW3bsLpZ0bmeyXRCH3n6uk57EaQ+BWfPL3FHlg++9CgGnRzLk2OOjo6QsmDrWgRDEaPrvjf4SG2aKv6abQOR8cmmFmPJzwTapgMfqKpSSwFFKGJNRjAe7zqCH4AzBOV/7PqOy4tzmu0a13cYAk2z4ea6yxPXpWa7QbENVVkymdQUZYEVS11X7LIVaxpOw2QpcODoQ898WlFU2so9rbK6iocYi1K6NhGtUnQ7gKn07ryme0OcXcHhYBCoaF6nStA0C1OQcJwpyB2YvI/uQHrHeq4cIIzn7qOlkayGzKq8J6PGmqGGIS9akutJtKO2skfpYrVrvaZUsY9pbUnNl432gPDO86rjpbUPIvLXROSJiPzeaNt/IyK/E//7toj8Ttz+FRHZjL77T1/5St6MN+PNeC3G99RLMoTwL6W/ReQvA5ej/b8ZQvj657kIiWCNvOKHQePu+/d3WQF5vR6b/q9oBaTPL7YC4vZ8DhgwCqlmQ1fU3jlEfKYn63vHLDZBbZuWtmm54YbpdEZd12w2G5zrefjooe7vAvPFMY/efV9LZSNKrppPMWUZ4bc+1/67dkvbaGCsriusWEqrbNLOO4Kz9E2XqxeNGO0aZWMdg9fcPybQu4a26ZnOJnnVDs6z3q45vzjn/PyMZ8+ecXVxgQ0wm0zAx+7McZUt64q6rhGBui6YVjWgaE8rFgnKH+HS8XPQTSHTxmq3KpfM8wgpHkxofffKPi3sr20pVZeff/ydsQZiN6U0EayxkWjX4vEMjMupq7TPVsIwv8jB8TGFWjr3uJoUSVZv3J4s4VhH4aMlYYXMHWlES7u9J1PQQYpP7MbG2LOgs3UTrWgjGuzt+BPqJSl6t/8i8E+98hkPnyM3zByEddcdkPxwJUJQbwt82j8dMx3nxQJPPs74HEMcYyTwoxOE0TmSeUj0J8UIdV3n7MNqtaLve46PjrDW0rZthkEvlwtc7/jGRx/z6NEjAL701a/x8O33efDwLXqncQeNNCvdlgRLYat8fBfTfz5otFzqGojdkcVqHULvkGLsDqSJxS1TvrCAdzQrpW+7uDjj/Pycb3/4LS4vNHOymM8oypK+a5RnEp9NY4Pguy0+eKalRfAEp+ZxNVHmYt+NTHVJbr5gSkvXtTlqn97fmB49MBRjjeeBvrvdyD9A7z0mImYj6CJmIIb3n2dcGM6i79XngKUGkiPCac/8z8VNoxLlxKGIEIONIac4JUKsTVli0GI/F90tYl8PCCMlYnYWoRCiwmGkJPZS7fl28vevNr7fmMI/CjwOIfzRaNtXReS3gSvg3wsh/B+Hfigivw78OkBdFvGlx+YZUTAh8g3cucrfFvh47JHQmzsFPu07XuX1qIcFXj+HnZTTuOAlHUZk6CsAsFwesV6t+Pg7H2OB2XROCIH19TWVKZhNF5ycPGQy1w5Ls6V2ZHYpGyBKWuKD4g+sUVLY1PcwpDp6URCPkVjxV5T0EQpsjc0TTKm8epzzFNbnVSfhCbabDevVTcZZrFfXWrS1WbGYVEyOFwiKQlX4rWXbdqRW9NV0St/19L2LfIYBFVZ9dsrH6aIlRy7aGUvaOKTkvRtAQMObTwtlFkTv3WibMP5JisALiWw1nkD0WD5WvCbggerLIfskYgYLMUlj+jse3/sYNI1EDYLg0bhRUeg5BxvX0/eRddo5ggtUZaG8CZEpW19LZHPe40Ow1gwB1r05mJ6f94Gu7TTwOuon8bLx/SqFPwf816PPnwJfCiE8F5FfBv57EfmFEMLV/g/DXtu4lL7btxR8CHDnKj/uy5COfMcqf0DgYVfoXybw6VDpPOl6UqRXS2cVZXd1dZX3XcwX1PWEed/RNRuKmPZztuTy4prTBzUPHj2KeHaFrvauz+Z+Srv1bZ9BMr3r8THFKEgsmy0AM7p2nRiud5Sl4CIHpBGJ2ICGVdezbbas1yvWqxXb9QYJQanM0oS0okHJWKuA7wkh0LgOPBnlmFZmrRcQqlIJX0WE4AYrUCf0sNx6konukVHOPa3IGRQ0yjD5METejYlAI+e0db0xMaORrDtR9yAGpY2xUSENgcOQX2l6r7ox7e+9p4i8lG3XxjkysE1ltOOI1FWE+K7TojIoE52nIYPCiP9UZclms9Wg6yhaqsVOwj5ZSpq7yVoYCrdST4pIPPs5mFO+Z6UgIgXwLwC/PLrABmji339XRL4J/DTamfrukXIwewIfzzMS+hcL/HCosZDvC/x4O7dW+Xh3+dypLDm/dCN5Jbbp31SZlnzMEJjP56zXyfy+ZLGYM6knFCL0Xcd8Nkdmhpv1hqvra6bAyVw5FOcL7dVoRGjbLhYTObUUvMk1Bum267LKvnXbafFQ13aUZUnbtXRtR1VbjmJB1GQ65fzsGZeXV9xcX3N9c62FWgKFEYqYSbE5Uh5XeMBFUzelxVJZ9XQ63enjgAzFRAFN1Qbn8b7HmELfczy8d3pvzvvIMEQ0edO5lag0LxdeXYKcqXAxbx8CAZOj8YM7Y3TPpAC8J5iIB4iCk959LkGORKqgsYiu73NlpI30/V3T7HR0yhkq7yAop6IWpRGP5UnRLzFq3aU2dAbtEKXzS3LF40B7N2zbXQTTfA14P0h+wid47xjTub7K+H4shX8a+P0QwnfypYk8BM5CCE5Evob2kvzjlx1IRLH6twRevzywyh8WeCAy7O7WsafDxAPmc0JyT0zEtqvAK6OuzeWzMAhgEvqETw+dp8t+uZJkeOdZLBd5ZXvy5AmrtVAWWkHp+56u10rCt95+h8fPntH1jsk0shUfH3NydEzX92y2W6aTibYhL6u84KSJCTCpaiWpEWG1WvGHf/ANur7jeHkUU5SBqjZ55d9s13z23U85Oz+j2W6ieatgokk9x6cYxQ7aL5J6oFZK8A6JRUQhDIg/UKpzCRJjGOq/E1f1zsXAWmHzqulGVlna6IIQonAFEXofGMh8Y9lyCDHgus8EPiiUNJzrKUslSymsVQblHMAO9BFSnIyL8QKV08tRMW022+yGjElN0rMyEXnYO20mIyZavIFclJYCmOqWaHzBGqu0bnEujucpqDKqyjK6W320toiyEchQdYbFdGSavPL4nnpJhhD+C+DX2HUdAP4x4N8XkR5VlP9GCOHsFc6R86y7Ag9joT9k3uvvd46WLQgjglgN0GTz3gx/wxiMNOSHNUDk42QdBB7IQp+IK7xXRUAYMiEhBqnqiQr5w4ePuLq+ZLVeYYwwnU7otp0y7MT7f/jgAfcfavbh+PSEup6yPr/AlqVaKnHy+FghR0QAAlT1FGuFi4tLzs/O+c4nnwCB9WqliLjgFHQU19pt03B1dakBTGA6nVDX2rfB+Q4J2rFoUM2e4Ik1BZG+PGgVolYaGlwweeV3QZmYrPKuDsazGHrn6OkpKbGxMUbbt9Ey8DivVob3qXmrRGIUv5Nr1+Ihm5VDXkU1SDDwc6b9Q9DqyFziHXK80MeYggTN/KQxDm6KGIqyyIoyuxs5/qxmetephWatpZRSszoSIhXrMJxzVNUEjxZY2fh+O+8pZQCNmVHcxdqIOTGCD5qVIV1HOBD9HI0faKAxHO4lSQjhXzmw7W8Cf/OVzx6HD57tdnvQvIfdVT4LfPSvxgIP7Ah9FvgRm/Mg8KlHH1HA0yrvcuuufYGP9zikqLJfq9FhkwOlls1mkynGH731FiKw3qxx3rPebCmLEovh6bOn9C5w+vBBbJYKs8USF2G701hP4JxTrgSnUXUTBQzg+uaGbdPwyccf8+l3P83Cfvb8GVVpWS7mEBzrjTIvbTcbwFPXJd5BVRVYo+Yr3mNRZZqp3r3kQoMQQgTaCGKtgqKMIXjwfVqlIi+CGKwp1ISWjt51UZAM266hju+n9z1ibJYxUU2MhMjw1Pe4pqOsyjwn1KKLGH+JWZTexbkxuJ5p2FTEFC0WH9KqOmJdCn70m+Gdm7hyb9Ybur5nMqkJIaYz7ZDm1bnb5QVjbOqLtZhAJiSOZ8hzWiL0OvfIuDXUKmKcZchW9G2FEMJAAvM59AHwmiAaQ1DmpX2B14+HV/lDAg/sCL1mJgbhds5p3f8oGpsq6Mar/EGBH1kWIinoMwr8hN2VqSyHR/vk8WNm8zmnp6f0rufi4hLvOiaTKWIMdVWxXB5lerK2bWk2DcZYprM5bdtEEg5drcWrebrZaK2Ec4EnT57w4Ucfsr654fTkBGuFm+tLJnXFfD6jLCwuMhE5p0rDiODpESkUl++cQo2DTtUMEzYDjsQHEAmICbRtj7EVfZ8yI/pO6rrWOEtE/Wm0Xi0BQTEKGKGPSjNlRqqq0oBq3xOctmUToOsiHLsYlIJ3PrtR43mULMixQkgBROd3I/CZ/zMKp7GGvmnzj1IcK83Hqq6ZzqYxqt+quxNhBA5HVVdMJhO6rhvYqKwBUctrzH2gwhryiqd/m6ywdq4/3ds4m8JtVaDH3HcXDu354vFaKAUjhtl0FrX5IPAwenExVqACrkEfnwQ++vJAFnoXg1cvEnjYFfpXFfjdEV9gKnYaaefk39mi4Pr6mr7rWJweM5/NWa3XrNdryrJmvlzumOvrTUPTNNovsbB0nQpk7xxibIRHe9o+AVtUoI6Pj5nUFXVZ4PqWSRU7RBPoXaf4e7TWTl2RHtd1mLrOvnTwHufR70YFRSbukP5vjMF3yqFQlsr2NImU8ypiGntwrmM2n7PdXFNYidv0uQ8cjkp02nV9rkfQVx4IoY8MSz63lheJbktQRicVmBHzU655SOadgGhxmYjQ9I1ahXE+SOSfKMsK341dhvTvEKuwtuDkeIEPgZvra+12jQKlyrLEllYXOBlhA6Lwi4zm0S05ja7wyJKQ8T2w5wLsWAq3D7pjae+f6iXjtVAKiOZ0nXM6CaLAw+0Ank9/HxB44M5Vflfgza4mvlPodwVeLzWlzHbxD+PO1RJ9167VVad3jrqqWa83OAL3792nLAqePTujrqcsFwskSCYRcb0Su0pltEGICJ3zGGBWT7i8uqLv+hyzCD7wznvvcXxyxOr6ivXNFddXFzhxtF1Du93kVRo0q1AaTWGW04K2bTS1JuRJ5pwb2rIXFhtMvm8NyBomVUkQzcan+gEAF5y6HMHTdA1VVTGbTnGhU4RlCtJmfgQbYwaaIFDClZAVqwaCQ3aX9t+PMYZCCqTS6X+7g7lHIndmplfzPjau1eO63tH6JrsoJnaNHoRsOOcOmG4npuBHAb7xVd4O9yXwVfo+b41B0hQjHOuVYWEbAokvE/YMdPoc403fhzfjzXgzdsZrYSk457i4uBgi+iOmnFfx9RUwNLR937EEXmj6R408Nv0NyBgFGSm/sim599lFOi0fU3j5+iFXPRqnq2NdV2xWa7bTGUfLJfPpVlfoAJv1mqKKK3+kFncuZCtJKcUViJT96PiMrLUcHS1YLqc8M4HLi+dstlsmpWE2nXLdN2y3TV512lbZkqqqRMRQlqXGX3qtCSjLCmsksgGpaWxMSV3XeOd2uhtr7EGxE8nbMp4Btu4D2+1aTfP8PlLkP8WBUkYlMvKFYftmu8UaQ1EU2f3xwWt8wZBdxb5XFGBhD1Ui6pwpjJK3GGOQvoeU2vP6bHeymJJW2d1AXrJyTGpnl4z8EIaYxXhhTj/P8zHPvBhH2J2PLyZDGcckdk9yINT4guO8eLwWSsE7x2q9uiXwQG6wIcj3JfAKQtKXOAZ/iBnouF4m8EAW+p32dGMXJqSIIEzqCaA5/BQPKYuSs+dnNJuWB6f36XzAd47V1Q1tp0JYz6aaigzC2ghN09K0CpTZrNc0bct2s83P4eHDBxEl2PHpd7/D48++iyUQbI2EguVyTl1X9NFf7vqW1UpjGpPpFGO1x6IttHBqdX3NZDbJ5ewq3H4n+Jju1XkHTid4G90lEWGz2UROhpKu6xUcZTVOICOMnz7TkBuggAJ5bDT3U4DXWptLxUUk90RIwK6uiyXFxW436PiWc0Q/md76klNAL4nU+KpuzyyRFOcY4JIK88IAABB8SURBVNJjpK22zhs+j/36/ZTgLfc1BDTdkgoDB+Lb8TGU7m0nG/pK8v95HIjXQimICHVV3RJ4uPt+Dwk8sCP0uwI//A3kwGSIqUdNQSp3gXfhsMCn64pyL3EFSOc0cbIJiYFJU4D1ZKJEI0UBAuubDefnZ9w7OqEQy3azoW17vL0A4Pj0lPnRksvLK01lGaPkpU1DUVjapuWjjz7kK1/5KgB90+DaNednz/j9v/970Pc8enif4HpubloKU1CVJbPJLN5C4Gh5zNnlOZdXV7SxjfvRcomdWFzX07WtZiLQZ+dDoG0binLIACRZSgG+cSepqq4yqUkZgWnO9zGInLov6dBgoQqBiOTU4nQyUcCRCFW0onROBBJ4OFknktIAIcv7zsgEqyFVK+76+kP8YXc1vp3Oi82Kiv0aGr0uGQWM03yRA3nBvCjtH/6OkYKvcUKSyGj2x574DPL0Qgtkd7wWSoFxpJ9B4IGc+x8L310Cn38bNNV4S+Djip9XdHbdk/2sxC2Bj18kV4M9V2P/GpuY3urajrIodHUTQ31cUdiC9WpN8ND2DikKXHrLBlrXaZfjoqCwSpLhg+d0eZ+zzYYSURMYCN2W86ef8uTxZ4SuYTmfUxUF1hjatmWzXrMOnjrWlxRlycn9exRVgS1LZSXuejbrDaW1HB0f0bsuK85EluJi2iBNtKAVQLo6j1Zm55wGFiOpqz62AcORalZCLj6KkOhYC9DFQJ6JRCGp2clYCnZTcTo/BoEeZ0rivlmnh2GXqNmVXn7XTtgdw9aEoRkQlKNrCGPuxeGXw6lGSiYrigGTMxRsjayPjCpNOmFsHe8KerR98pmH1OznCzS+FkpBV4LqlsDrd7sKIIRUIDPgD5KJD+wI/csEHtgR+uQjygEltL9/fu/j3+0uEDk7YMRgi4K2afAhYEUrGm1d0G5bvHjaZo2J2IY+tFytzglBqOspXaTaKgpDXRfUZUldlXSxDdzVRWDbXHN+9pyjxZz5fJ7vaVpPcL22ar+6fKbPyHmub24op7UqK2sJVYzjeDg/O8OLslKDKpGmaaJwhSxIsbhfn3Egswl772i6lsJGfsKQYi0hugD6vFJQfCxC6dsd6rQoCCbvNRrjD/tpuP1lM4poOkeyHvaFeJiXh/8eZxmGxWzgXxhnJUZXw8uEcwg/HDB1opD70b0cwmIcMG0+93gtlIIRySixscDDbb/+ZQIPh1f5cV1F+k73PbzKp4d8SOAheREh+9v+wPUkZJ2tLKnvYu8cHZ5KBCuCN2AKg2s7rq+1gEoqw/L0hL7tubm5woqlKguCt1xdnNM1G8rC0DaaI1/fXLFaX+Fcx/HREXWlxC0eKCPWYblccLRc5Gu/Wt1w/fyMzvVKQV+WVEVJWRTMFws2zWaHebiwCpL2IWCJtRexGrKPVsQY/993HSGhIOM7VkHXfL3shcYGM3fwx/fLpfdt4325SyvpuNHMeKRGuMNxIJYj6tUcCPwdGikluRtTYEcpjN0SQS0Evaxksgz3HMJgLYAugnejGuP8YpjL7AVDd69z/0m/fLwWSsF7z9XV9S2BB24J/ViAsw8/0tiHBfywwOv+h1f5QwK/fz23lNJe4DOhGoOPlYXWUEhJ7z29d7nuoahKZsyx0bIoyoLz8zPabUdd1kwnE3o8rodPv/sJRsAa6GMX5r5tNCwl2ntxu214cP8evvc8f/6cvuuoipJFrJI0Rs95au7RdMnFaQlOa/wXsxn37p9yfa1dqttOezz2XaeT1d6mgr+FrY+TdWSlDzMz/r2/kOdallxfEXYn/o7Ajg5AWv+Hw98SEhkUWl4cghKaHHA2RkcaHScMwj8ELIfzK09FUo6yc5zUKPfWIxpZEC9DNY5djfEV7x9Tj7V/old3IV4PpRBCZhdKPnv24XNG4C4BHz7DnmswFvj4bscCD9y5yh8SeDiscSWdeLRCyPCTWM+uEfN6UrFt29hAFcqiwBitfrsXYc7GWj55+hliAlVlsMbjuw3OOwoxGUKcS3uBaVVRz6ZcX19xfnbOfDalsAXL2Yz1ZqMUcDc3+XpsWbA8PmZ+tMQ7x3q9ptls6JoucxWkgivaNt9jXtXC4LMqiCbsznlJq3ZKXx4K2h0ewkA3lpT9/m+TI5D88PGquw+/Gdz3MNqH/DJN7Mw0frljg2J8Tkhs0T53rCad3/vYTq/I83Ow9kNcwPbm0Y5XkdyZw/AhtaR3TeLbCnmYgS9TIHeN10IpGBHqurot8HCHEhhvZ1eZv4JZv59NGFsm+2Ms8PkztyfM2PogpidSWqzvI+kFxO7GsSo0IuZC6PGezL9gq5KT4yNd4a2lbzv6voPgkaKMnYM8Cc+2WW8whWW2mHPv9B5VWXJzoxWS90/ugzFKgTaqqss+ewiZiSgZmqnT1Ti4qv8Ms/zW0wrj+0+bRmGv0e8OT1DJxxeTXI306JVYxoYDwhLNkF0kYTIr00e9r2Qp7AqLsBPJz/pir83b/p1FRZdcROLq7iIlm+xNzIOz647nKYbRvccz6sQezeXB9X2ZrtXT/IhZCinQuC/w8c/88HZW+QMCD+wK/UsEHgahH5tqLxZ4SEK/M9J5R5WeaZTlUHBkCkvXO8UN9C0Sip0oth7L0TtlMqqMUFjBTkqFPqPWTWGNgpyAdd/R9Zot0Ci+IcWcfHC5icpgnpsMtBpWsOjLQoQgh4wVyeYsydwdnndes0dLbbLQ9guTZDSDd9yJ0TNPq3F6lmmK7kbdb4/h94NBfusVhTC0eduz6sa/1X31i4MAppDKmg/EFA5RqY+ONd6WXI794MidlsKt/yfFo1tu3/PYX3v18dooBWPsLYGHl5v1LxT4KMwvFPhbG+8WeNglWQmEuKLFhh4ikfcgEnwmkg6vLeh9YanthKJU0I0zenBjRFmSMvuzovOsVR6BwhhVgC4KJLuXKNbgUudq2W0m6lyI1zFg7TXH77NFMKTX9HpVoQXGJchDHn/3eQyPUDLzUnqkuSXbeJ/RRN1XBMnFkJEwqts+tsMHt2F8rqF4bXwBe79JXAjjWAWHAEzs/T26TwYuD4HRMxrIU1Ks4XsFMMlLAEzBh0zftud5/EDGm9qHN+PNeDN2xmthKfjgaeNKt59tuNPXP2AFDNvv2nDACtCTRFN7bAVoFV8m+9zhU0grqzYUTd14jBglJzGpN2JceV2gaRratsX3jnJaj+jUdoOi+jwGwzCbseNb2vvXRCc0kYQkBqKAugjWKHAo0bgbEToXKxWz9To8y6F7ssnnGQcVd55bNMO1MnRwH8aWSQpQ5gU/m+MpZjGm8Nf3lBf6HEA8/N7Sk9g3zQ8Nn/pMhJCDi+NdD63qewYHKSLpvYeiYHAfJMc+zOhd5vs5cBM5ZHP4cm+NXdSl9r24a4zv7XYW48XjtVAKIQyNO2AQeP37Rb7+7gS96+C3BD4KA+wKfaqt0H9LTDkogMSHmIhaVehNJtl80YN30tN1UeCSYkqR9VGfv8EUTMKUjNgwulchO8ZxJEbgRPk9zk374LCUSrWWAo1GCL3yCyRhzahRGVB74yBaCjyqC3Bb7vbf0yGIcM4qvGAMpr3JC4Re09CGLz2pHVlN1zZWTAfcwHSObCSH3fhC2Pv3kMgmt1VEBv4DGc65n04MYXg+Q5aEkcYZYjQ5m7LH0ej9oCjVzY6Xtr8opnPmONyh1OaLx2uhFNQ327vw/Rs5NJn2BB72+jK8SOBHXZ4yUasZFEDiI0zVcN//TcoQEI16wRjB9+RA1ChUPzyYgzK0G8STyBHgemWbshFpiFfmqapQVqHcJCj6pcNz02eTfXOd9ZmlWuMFik9IVsOh9zEI/G06tOS736UTkouffHXdd1De40DjrbcR7vwwXP+BE4sMqEatL7kdJBxuYTeTkmIHKUOToO8pQHoI1fgidbj/ndmPxI7OPcRuDgj8vna79cXLx2uhFA6OUWDLqx2fefXuEnhgR+h3BH60qqvAR7ThiKf/hzZGZncOUCYhSfv48fsMkRJxaG2eFoUsJKPD2+jSuL6LLdiSQKbJOwTEIK1cxNRmEgpdycUIOLWodrIPsCMUyQtIukxEckFUvOWdEUIsZ8m1A+HWNL2FahxDnff2u2VdxQ+q50LEENx+VkrQOi68Gx5uVnhwGOuzY5hGlyc9o8z6PXTB3tmXwdrKJw67SofRu0YG9u3RWSEF48MBVOOOdbQXVP0c47VQCiEoFde+wAN3rvK3BD41wZBB6K0ZdQL6Aoegmt9kPzvl4ONEIoJSdsPx0X3I02RHGMZwXomZgz4xPTOY+T4i6cbKLwnamPFY4vciBk+v1PCj7IF2WR5NQNK1ceva0sXnDEK+pX1LId3X7RhSQjX6vOqONOJYkMZuy201M+yXnufIEsmLcXYfdi2b3eMM2jgpjxBG/AcyPIOcrtyLBB1CNaZjhxDdshAgzm3f31aIegkvcm5uXbF+/hyKQV7m4/1JDBF5CqyAZ1/0tfwQxgN+PO8Lfnzv7cf1vr4cQnj4sp1eC6UAICK/FUL401/0dfygx4/rfcGP7739uN7Xq443OIU34814M3bGG6XwZrwZb8bOeJ2Uwn/2RV/AD2n8uN4X/Pje24/rfb3SeG1iCm/Gm/FmvB7jdbIU3ow34814DcYXrhRE5FdE5A9E5Bsi8ue/6Ov5foeIfFtE/h8R+R0R+a247Z6I/K8i8kfx39Mv+jpfNkTkr4nIExH5vdG2g/chOv6j+A5/V0T+wS/uyl8+7ri3vygin8T39jsi8s+OvvsL8d7+QET+mS/mqv/kxheqFESpff9j4M8CPw/8ORH5+S/ymn5A458MIXx9lNb688DfDiH8FPC34+fXffx14Ff2tt11H38W+Kn4368Df/VP6Bq/1/HXuX1vAH8lvrevhxD+FkCcj78G/EL8zX8S5+2P7fiiLYU/A3wjhPDHIYQW+A3gV7/ga/phjF8F/kb8+28A/9wXeC2vNEII/ztwtrf5rvv4VeC/DDr+DnAiIu/8yVzp5x933Ntd41eB3wghNCGEbwHfQOftj+34opXCe8DHo8/fidt+lEcA/hcR+bv/fzt3zNJWFIZx/P9gaQfpVurSDiLdQ6eCUJw6dHMQnBQR2iH5Dl27dO5QcFTIEnQo7VfQVZ1LESXZXDoZ3g7nBHMlAdOUe0x4fstJLhmelwMv55x7byR9yNeWIuIKII/Pi6Wbzrg65mUeW3n7sze0xZuX2u6tdFMY9UD2rN8OWY2I16QldVPS29KBajAP8/gVWAEawBXwJV+fh9omUropXAAvh76/AC4LZfkvIuIyjz2gQ1pqdgfL6Tz2yiWcyrg6Zn4eI6IbEf1Ifwz5jdstwszXNqnSTeEEeCVpWdJj0oHOUeFM/0zSoqSng8/AO+CUVNN2/tk2cFgm4dTG1XEEbOW7EG+A68E2Y1bcOQNZJ80bpNo2JT2RtEw6TD2uO1+dir46HRE3klrAT2AB2IuIs5KZprQEdPJrqo+A/Yj4IekEaEvaBX4DGwUz3oukA2ANeCbpAvgEfGZ0Hd+B96RDuD/ATu2BJzCmtjVJDdLW4BfwESAiziS1gXPgBmhGRL9E7rr4iUYzqyi9fTCzB8ZNwcwq3BTMrMJNwcwq3BTMrMJNwcwq3BTMrMJNwcwq/gLHXCpso+SM+gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for im_batch in train_generator:\n",
    "    for im in im_batch:\n",
    "        plt.imshow(im[0])\n",
    "        break\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1000 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "test_generator = test_datagen.flow_from_directory(test_dir,\n",
    "                                                    target_size=(200, 200),\n",
    "                                                    batch_size=20,\n",
    "                                                    class_mode='binary'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.Sequential()\n",
    "\n",
    "model.add(layers.Conv2D(64, (3, 3), activation=\"relu\", input_shape=(200, 200, 3)))\n",
    "model.add(layers.Conv2D(64, (3, 3), activation=\"relu\"))\n",
    "model.add(layers.MaxPooling2D())\n",
    "model.add(layers.Dropout(0.25))\n",
    "\n",
    "model.add(layers.Conv2D(64, (3, 3), activation=\"relu\"))\n",
    "model.add(layers.Conv2D(64, (3, 3), activation=\"relu\"))\n",
    "model.add(layers.MaxPooling2D())\n",
    "model.add(layers.Dropout(0.25))\n",
    "\n",
    "model.add(layers.Conv2D(64, (3, 3), activation=\"relu\"))\n",
    "model.add(layers.Conv2D(64, (3, 3), activation=\"relu\"))\n",
    "model.add(layers.MaxPooling2D())\n",
    "model.add(layers.Dropout(0.25))\n",
    "\n",
    "model.add(layers.Flatten())\n",
    "model.add(layers.Dense(256, activation='relu'))\n",
    "model.add(layers.Dropout(0.5))\n",
    "\n",
    "model.add(layers.Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 198, 198, 64)      1792      \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 196, 196, 64)      36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 98, 98, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 98, 98, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 96, 96, 64)        36928     \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 94, 94, 64)        36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 47, 47, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 47, 47, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 45, 45, 64)        36928     \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 43, 43, 64)        36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 21, 21, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 21, 21, 64)        0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 28224)             0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 256)               7225600   \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 257       \n",
      "=================================================================\n",
      "Total params: 7,412,289\n",
      "Trainable params: 7,412,289\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=keras.optimizers.Adam(lr),\n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['acc']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      " 17/100 [====>.........................] - ETA: 4:26 - loss: 0.7037 - acc: 0.4794"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m-------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-21-156aacfada04>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0msteps_per_epoch\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mvalidation_data\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtest_generator\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m     \u001b[0mvalidation_steps\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m50\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m )\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\kr\\lib\\site-packages\\keras\\legacy\\interfaces.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     89\u001b[0m                 warnings.warn('Update your `' + object_name + '` call to the ' +\n\u001b[0;32m     90\u001b[0m                               'Keras 2 API: ' + signature, stacklevel=2)\n\u001b[1;32m---> 91\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     92\u001b[0m         \u001b[0mwrapper\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_original_function\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     93\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\kr\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit_generator\u001b[1;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[0;32m   1416\u001b[0m             \u001b[0muse_multiprocessing\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0muse_multiprocessing\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1417\u001b[0m             \u001b[0mshuffle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1418\u001b[1;33m             initial_epoch=initial_epoch)\n\u001b[0m\u001b[0;32m   1419\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1420\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0minterfaces\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlegacy_generator_methods_support\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\kr\\lib\\site-packages\\keras\\engine\\training_generator.py\u001b[0m in \u001b[0;36mfit_generator\u001b[1;34m(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[0;32m    215\u001b[0m                 outs = model.train_on_batch(x, y,\n\u001b[0;32m    216\u001b[0m                                             \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 217\u001b[1;33m                                             class_weight=class_weight)\n\u001b[0m\u001b[0;32m    218\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    219\u001b[0m                 \u001b[0mouts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\kr\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[1;34m(self, x, y, sample_weight, class_weight)\u001b[0m\n\u001b[0;32m   1215\u001b[0m             \u001b[0mins\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0msample_weights\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1216\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_train_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1217\u001b[1;33m         \u001b[0moutputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mins\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1218\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0munpack_singleton\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1219\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\kr\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, inputs)\u001b[0m\n\u001b[0;32m   2713\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_legacy_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2714\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2715\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2716\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2717\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mpy_any\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mis_tensor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\kr\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py\u001b[0m in \u001b[0;36m_call\u001b[1;34m(self, inputs)\u001b[0m\n\u001b[0;32m   2673\u001b[0m             \u001b[0mfetched\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_metadata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2674\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2675\u001b[1;33m             \u001b[0mfetched\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_callable_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0marray_vals\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2676\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mfetched\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2677\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\kr\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1437\u001b[0m           ret = tf_session.TF_SessionRunCallable(\n\u001b[0;32m   1438\u001b[0m               \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_handle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1439\u001b[1;33m               run_metadata_ptr)\n\u001b[0m\u001b[0;32m   1440\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1441\u001b[0m           \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "history = model.fit_generator(\n",
    "    train_generator,\n",
    "    epochs=30,\n",
    "    steps_per_epoch=100,\n",
    "    validation_data=test_generator,\n",
    "    validation_steps=50\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "keras.optimizers"
   ]
  }
 ],
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